IAES Inter national J our nal of Articial Intelligence (IJ-AI) V ol. 14, No. 3, June 2025, pp. 2236 2245 ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i3.pp2236-2245 2236 Camera-based adv anced dri v er assistance with integrated Y OLOv4 f or r eal-time detection K eerthi J ayan, Balakrishnan Muruganantham Department of Computing T echnologies, School of Computing, SRM Institute of Science and T echnology , Kattankulathur , India Article Inf o Article history: Recei v ed Jun 1, 2024 Re vised Dec 10, 2024 Accepted Jan 27, 2025 K eyw ords: AD AS Computational comple xity Correlated outcome Real-time object detection Synchronization rate Y OLOv4 ABSTRA CT T esting object detection in adv erse weather conditions pos es signicant chal- lenges. This paper presents a frame w ork for a camera-based adv anced dri v er assistance system (AD AS) using the Y OLOv4 model, supported by an electronic control unit (ECU). The AD AS-based ECU identies object classes from real-time video, with detection ef cienc y v alidated ag ainst the Y OLOv4 model. Performance is analysed using three testing methods: projection, video injection, and real v ehicle testing. Each method is e v aluated for accurac y in object detection, synchronization rate, correlated outcomes, and computational comple xity . Results sho w that the projection met hod achie v es highest accurac y with minimal frame de viation (1-2 frames) and up to 90% correlated outcomes, at approximately 30% computational comple xity . The video injection method sho ws moderate accurac y and comple xity , with frame de viation of 3-4 frames and 75% correlated outcomes. The real v ehicle testing method, though demand- ing higher computational resources and sho wing a lo wer synchronization rate ( > 5 frames de viation), pro vides cri tical insights under realistic weather condi- tions despite higher misclassication rates. The study highlights the importance of choosing appropriate method based on testing conditions and objecti v es, bal- ancing computational ef cienc y , synchronization accurac y , and rob ustness in v arious weather scenarios. This research signicantly adv ances autonomous v e- hicle technology , particularly in enhancing AD AS object detection capabilities in di v erse en vironmental conditions. This is an open access article under the CC BY -SA license . Corresponding A uthor: K eerthi Jayan Department of Computing T echnologies, School of Computing, SRM Institute of Science and T echnology Kattankulathur , Cheng alpattu, T amil Nadu, 603203, India Email: kj4134@srmist.edu.in 1. INTR ODUCTION In the rapidly e v olving landscape of automoti v e technology , adv anced dri v er assistance systems (AD AS) [1] ha v e emer ged as pi v otal components in enhancing road safety and dri ving ef cienc y . Central to the ef fecti v eness of AD AS is the capability for real-time object detect ion, a task that demands high accurac y and reliability under di v erse and often challenging en vironmental conditions [2]–[5]. Recent de v elopments in articial intelligence (AI) are bringing the concept of self-dri ving automobiles closer to reality , with the potential to re v olut ionize transportation by enabling v e h i cles to dri v e themselv es without human interv ention [6]. The society of automoti v e engineers (SAE) denes six le v els of dri ving automation, ranging from le v el 0 (no dri ving automation) to le v el 5 (full automation), reect ing the progressi v e sophistication of autonomous dri ving capabilities [7], [8]. Consumers w orldwide are eagerly anticipating the introduction of dri v erless cars, J ournal homepage: http://ijai.iaescor e .com Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 2237 which promise to na vig ate comple x en vironments, classify objects, and adhere to traf c la ws autonomously [9]–[11]. A notable milestone in this eld is the Mercedes-Benz dri v e pilot, the rst autonomous dri ving sys- tem to recei v e complete certication at le v el 3, marking signicant progress to w ards fully autonomous v ehicles. Self-dri ving cars utilize an array of sensors, including radar , video cameras, light detection and rang- ing (LID AR), and ultrasonic sensors, to g ather comprehensi v e data about their surroundings [12], [13]. These sensors enable the v ehicle to construct and continuously update a detailed map of its immediate en vironment. Radar monitors the positions of nearby v ehicles, video cameras identify pedestrians, v ehicles, and traf c sig- nals, LID AR measure s distances and detects road features, and ultrasonic sensors detect obstacles at close range [14]. The inte gration of these sensor technologies with adv anced computer vision systems is crucial for the performance of AD AS, as t hese systems must process real-time data to mak e instantaneous decisions [15]. The demand for AD AS is e xpected to sur ge with adv ancements in computer vision and deep learning (DL). Modern automobiles increasingly rely on camera-based en vironmental sensors to identify , classify , and localize objects accurately . Consequently , ri go r ou s testing and v alidation of camera-based AD AS functions are essential to ensure their reliability and ef fecti v eness under v arious conditions [16], [17]. Current AD AS test- ing methodologies include v ehicle-le v el eld trials and hardw are-in-t h e -loop (HIL) testing [18], [19]. V ehicle testing on pro ving tracks v alidates AD AS functions b ut f aces limitations re g arding safety and en vironmental conditions, resulting in reduced test co v erage [20]–[22]. Con v ersely , HIL v alidation of fers a more comprehen- si v e approach [23]. In HIL testing, v arious scenarios are created using simulation softw are. These simulated scenarios are then fed to the AD AS camera via a monitor to e v aluate the system’ s performance [24], [25]. This method allo ws for thorough v alidation of AD AS functions under a wide range of en vironmental conditions and safety-critical scenarios, ensuring the system can handle real-w orld situations ef fecti v ely . This research delv es into the inte gration and v alidation of a camera-based AD AS using the adv anced Y OLOv4 model [26], [27], a DL algorithm celebrated for its ef cienc y and accurac y in object detection. The main goal is to assess Y OLOv4’ s performance within an AD AS fra me w ork, particularly focusing on its abil- ity to detect and classify objects in real-time [28]. Gi v en the comple x, v ariable conditions encountered in real w orld dri ving such as adv erse weather , this study aims to address the critical need for a rob ust and re- liable object detection system. Through a structured approach incorporating v arious testing and v alidation scenarios such as monitor based scenario projection, camera based real-time scenario capture, and li v e dri v e testing—this research presents an in depth analysis of the AD AS system’ s ef fecti v eness [29]. It e xamines the trade of fs between computational ef c ienc y and detection accurac y , of fering v aluabl e insights that can dri v e further adv ancements in AD AS technology . These ndings contrib ute to the gro wing eld of autonomous dri v- ing, highlighting the importance of accurate, high performance object detection as a foundational element on the path to fully autonomous dri ving solutions. 2. METHODOLOGY This section describes a frame w ork de v eloped for testing and v alidating real-time object detect ion using a camera based AD AS. It is clearly illustrated in the Figure 1. The electronic control unit (ECU) is inte grated with a well-trained DL netw ork. The frame w ork consists of four important units: in-front v ehicle infotainment (including a video camera and AD AS cameras), a central g ate w ay , a pre-trained Y OLOv4 with the proposed video frame feeding (VFF) algorithm [30], and an AD AS-ECU based object detection model. The o v ervie w of the proposed frame w ork is as foll o ws: both the video camera and AD AS camera are mounted on the v ehicle’ s windshield to continuously monitor the front road en vironment. Once the v ehicle starts, both cameras are acti v ated and instantaneously capture the road en vironment. This data is then forw arded to the pre-trained Y OLOv4 and the AD AS-ECU separately with the help of the central g ate w ay unit. The CarMak er (CM) tool creates real-w orld scenarios and feeds video to the proposed VFF algorithm, which processes the video frames and generates the object list to be applied to the object detection model. Similarly , the AD AS ECU pro vides v ehicle dynamic information for the videos recei v ed from the AD AS camera, which is fed through ethernet. The partner ECU then starts to identify objects, and the output list is pro vided in CAN format. The object detection model pr o c esses this and pro vides an output as a list of detected objects. The de v eloped frame w ork cross-checks the outcomes recei v ed from the AD AS camera as CAN messages and from the VFF algorithm in real-time. It compares the object list from the proposed VFF algorithm and the CAN data ag ainst the simulation timestamp to ensure that there is no f alse positi v e or f alse ne g ati v e identication of objects. Camer a-based advanced driver assistance with inte gr ated Y OLOv4 for r eal-time detection (K eerthi J ayan) Evaluation Warning : The document was created with Spire.PDF for Python.
2238 ISSN: 2252-8938 Figure 1. The real-time object detection testing and v alidation frame w ork 2.1. In-fr ont v ehicle inf otainment The AD AS camera ECU is primarily responsible for processing visual data in real-time, which is cru- cial for detecting and w arning about potential hazards such as pedestrians, other v ehicles, and road signs. Its ability to swiftly handle lar ge v olumes of data from cameras is vital for ef fecti v e decision-making and action in dynamic dri ving en vironments. It is mostly used for automating dri ving tasks such as parking assistance, lane k eeping, and adapti v e cruise control, all of which signicantly reduce dri v er w orkload and enhance dri ving comfort and e xperience. Additionally , it adapts to v arious en vironmental conditions, including lo w light and adv erse weather , to ensure consistent performance under dif ferent e xternal f actors. The video camera, ha v- ing similar properties to the AD AS camera (such as eld of vie w and frames per second), captures the road en vironment and feeds it to the pretrained Y OLOv4 inte grated with the proposed VFF algorithm. 2.2. Central gateway It f acilitates the o w of data between dif ferent components, in this case, the cameras (video and AD AS cameras), the pre-trained Y OLOv4 with VFF algorithm, and the AD AS-ECU. Simply , it refers as a central hub or intermediary in the system. The high bandwidth of HDMI supports the transfer of uncompressed video data, which is crucial for maint aining the quality and delity of the visual informat ion necessary for accurate object detection. Other hand, the processed data can be transmitted to the AD AS-ECU via an Ethernet connection. This ensures a reliable and f ast transfer of cr ucial object detection information, which the AD AS-ECU can then use to mak e realtime decisions for dri v er assistance functionalities. 2.3. Pr e-trained Y OLOv4 with VFF algorithm The m ain goal is to accurately detect traf c signboards object based on the German traf c sign recogni- tion Benchmark (GTSRB) [31], [32] dataset which is pre-trained in Y OLOv4 with additi v e support of proposed VFF algorithm that can be capable of performing detection and classication under v arious en vironmental con- ditions. In this process, real-time video frames are fed into the pre-trained Y OLOv4 model, enhanced by the VFF algorithm, to identify specic traf c signboards from a selected set. It simplies the process of object detection in a si mulated en vironment. It s main objecti v es are i) setting up the camera model in CM, ii) gener - ating video frames that represent the simulated en vironment, iii) pre-processing these video frames before the y are input into the object detection model, and i v) comparing the detected objects from the model with the data from CM to ensure accurac y . The model’ s performance is measured by its ability to recognize these signboards consistently and accurately across dif ferent scenarios lik e day , foggy day , cloudy , dusk, foggy night, and night. The ef fecti v eness of the pre-trained Y OLOv4 model, coupled with the VFF algori thm, is further demonstrated through occlusion testing, where the model successfully identies traf c signboards e v en when partially ob- scured, such as by trees, with maximum accurac y in percentage. The primary outcome of this process is the generation of a reliable and accurate object list (in this case, traf c signboards) under v arying en vironmental conditions and occlusions, ensuring rob ust performance of the object detection system. Int J Artif Intell, V ol. 14, No. 3, June 2025: 2236–2245 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 2239 2.4. AD AS-ECU based object detection model The operation of the AD AS-ECU, which is interconnected with both microprocessor unit (MPU) and HIL, and then connected to an object detection model, can be described simply as follo ws: The AD AS-ECU serv es as the central processing unit in this setup. It recei v es input from the MPU, which handles the initial pro- cessing of data, such as signals from v arious sensors and cameras. This processed data is then sent to the HIL system, where real-time simulations are conducted to emulate dri ving conditions and scenarios. These sim- ulations are crucial for testing and v alidating the performance of the AD AS-ECU under dif ferent conditions. The output from the HIL, which represents processed and simulated sensor data, is then fed into the object detection model. This model, possibly based on proposed VFF al gorithms, analyzes the data to detect and clas- sify objects in the v ehicle’ s vicinity , contrib uting to v arious AD AS functionalities such as collision a v oidance, lane k eeping, or adapti v e cruise control. This interconnect ed system ensures that the AD AS-ECU operates ef fecti v ely , accurately processing real and simulated data for enhanced v ehicle safety and dri v er assistance. 3. RESUL TS AND DISCUSSION This section e xplores the e v aluation results of the de v eloped frame w ork used for object det ection analysis carried out in a real-time outdoor en vironment. Its performance is analyzed and compared with e xper - imental methods conducted in the laboratory , such as the projection method and video injection method. In the projection approach, an AD AS camera is placed in front of a monitor to capture real-w orld scenarios, and video data is directly fed to the AD AS domain. This process calibrates real-time v ehicle dynamic information to the AD AS ECU for the object detection model. Simultaneously , the same video is processed using the proposed VFF algorithm from scenarios created by the CM en vironment simulation tool. This tool processes the video and pro vides an output as a list of detected objects. In the video injection method, Jetson Nano hardw are is used instead of a monitor , as in the projection method. A CSI camera connected to the Jetson Nano de vice cap- tures the synthetic video using the projection method. The detection outputs from the Jetson Nano de vice are streamed to the host PC as CAN messages. The host PC runs the VFF algorithm, generating an object list from the synthetic video. A comparati v e analysis is conducted between the laboratory method and the de v eloped frame w ork in a real v ehicle for object detection. This analysis focuses on indi vidual object class detection, synchronization rate, perc entage of corre lated outcomes, and computational comple xity . An o v erall accurac y of 97% is observ ed during testing under normal en vironmental conditions. 3.1. Indi vidual object class detection The e xperimental results indicat e that accurac y slightly decre ases in rea l v ehicle tes ting compared t o laboratory methods. In the laboratory , only 43 traf c signboard images are cate gorized into four classes: pro- hibitory , danger , mandatory , and priority . Additionall y , about 900 real traf c signboard images are cate gorized in a separate folder for training and testing. In the projection method, approximately 50 iterations are conducted to assess the performance accurac y of each object class. Out of 250 tested images, on a v erage, 15 are misclas- sied. Similarly , the video injection method sho ws comparable results, with an a v erage misclassication of 18 images out of 250, under the same number of iterations. This discrepanc y is attrib uted to the similar appear - ances of some object classes. F or e xample, the signs ”TS16-restriction ends o v ertaking” and ”TS17-restriction ends o v ertaking trucks” look similar from a distance of 70-100 meters. Additionally , the distance between the traf c signboard and the mo ving v ehicle can v ary under dif ferent weather conditions lik e day , foggy day , cloudy , dusk, foggy night, and night. P articularly in cloudy and foggy night conditions, the object detection model de viates slightly from its re gular performance, often detecting correctly only when the v ehicle is closer to the sign. Comparati v e analysis sho ws signicant v ariations in real v ehicle testing compared to laboratory m eth- ods, attrib uted to the natural v ersus articially simulated en vironmental conditions in the lab . V ideo cameras struggle to capture the nuances of real climatic conditions, af fecting the algorithm’ s ability to accurately syn- thesize the simulation en vironment. This leads to a notable drop in accurac y , especially in dark scenarios. T able 1 presents the misclassication results of the projection method across dif ferent en vironmental condi- tions. T able 2 pro vides the misclassi cation results of the v i deo injection method under v arying en vironmental conditions. T able 3 sho ws the misclassication results from real v ehicle testing across di v erse en vironmental conditions. Laboratory methods generally yield more accurate classication for indi vidual object classes, par - ticularly in day , cloudy , and dusk conditions. Ho we v er , in foggy day , foggy night, and night conditions, some misclassications are observ ed, with an o v erall a v erage misclassication of 20 to 25 images. In real v ehicle Camer a-based advanced driver assistance with inte gr ated Y OLOv4 for r eal-time detection (K eerthi J ayan) Evaluation Warning : The document was created with Spire.PDF for Python.
2240 ISSN: 2252-8938 testing, although there are fe wer errors in indi vidual object class detection, the o v erall a v erage number of mis- classications is higher compared to laboratory methods, as seen in Figure 2. T able 1. Misclassication outcomes of the projection method under v arious conditions Class Day F oggy Day Cloudy Dusk F oggy Night Night A v erage Prohibitory - 9 - - - 10 10 Danger 30 - - 29 8 14 20 Mandatory - 29 - - 14 30 24 Priority - - 9 - 16 - 12 A v erage 30 19 9 29 13 18 20 T able 2. Misclassication outcomes of the video injection method under v arious conditions Class Day F oggy Day Cloudy Dusk F oggy Night Night A v erage Prohibitory - 33 - - - 26 30 Danger 12 - 19 36 39 - 27 Mandatory - 8 29 - 35 25 24 Priority - 42 - 21 5 17 21 A v erage 12 28 29 20 25 27 24 T able 3. Misclassication outcomes of real v ehicle testing under v arious conditions Class Day F oggy Day Cloudy Dusk F oggy Night Night A v erage Prohibitory 2 33 3 1 4 26 12 Danger 12 3 5 19 16 29 14 Mandatory 4 8 29 1 15 25 14 Priority 3 42 1 21 5 17 15 A v erage 21 86 38 42 40 97 54 Figure 2. Pie chart of misclassication outcomes under v arious conditions 3.2. Synchr onization rate The misclassication outcome primarily occurs due to synchronization errors between the s yn t hesized simulation video and the real-time AD AS camera capture. This means the proposed VFF algorithm processes the entire simulation video through frame-by-frame analysis to accurately detect traf c signboards and generate a list of detected object classes, which is then directly fed to the object detection model. Similarly , the AD AS domain correlates the mapped object list, which is project ed into the actual outcome of the object detection model. The irre gular synchronization of data from the processed VFF algorithm data af fects the mapping fea- ture of the AD AS domain concerning the object list sent to the object detection model. Laboratory methods e xhibit more synchronization compared to real v ehicle le v el testing methods. The object feature mapping rate is compromised due to de viations in frame-by-frame synchronization, which is of f by v e frames per second in real video le v el testing, amounting to a de viation of nearly 25% in total synchronization. By comparing three methods, the projection method, video injection method, and real v ehicle testing - in terms of their synchroniza- tion rates and their impact on object detection accurac y . The projection method, with a high synchronization rate sho wing only 1-2 frames of de viation, results in lo wer misclassication rates due to its near real-time Int J Artif Intell, V ol. 14, No. 3, June 2025: 2236–2245 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 2241 processing capabilities. In contrast, the video injection method has a moderate synchronization rate with a 3-4 frames de viation, leading to moderate misclassication rates as the slight delay in frame processing can occasionally af fect accurac y . The real v ehicle testing me thod, ho we v er , has a lo w synchronization rate with a signicant 5 frames de viat ion, which results in higher misclassication rates. This is because the lar ger lag in processing and synchronizing video frames leads to a greater chance of inaccuracies in detecting and classi- fying objects, demonstrating the crucial impact of synchronization rates on the accurac y of object detection in adv anced dri v er -assistance systems. T able 4 deals with numerical representations of the synchronization rates under six dif ferent weather conditions. The v alues are represented in frames per second (fps) and indicate the synchronization rates for the projection method, video injec tion method, and real v ehicle testing under each weather condition. A lo wer fps rate suggests better synchronization and potentially higher object detection accurac y . T able 4. Synchronization rates under six dif ferent weather conditions W eather condition Projection method (fps) V ideo injection method (fps) Real v ehicle testing (fps) Day 0.50 1.00 2.50 F oggy Day 1.00 1.50 3.00 Cloudy 0.75 1.25 2.75 Dusk 0.80 1.30 3.00 F oggy Night 1.20 1.70 3.50 Night 1.50 2.00 4.00 3.2.1. P er centage of corr elated outcomes Based on the synchronization rates of dif ferent methods, the T able 5 pro vides a percentage for the correlated outcomes. It implies that the higher the synchronizati on rate (i.e., closer alignment with real-time), the higher the percentage of correlated outcomes, indicating more accurate object detection. The projection method, with the highest synchronization rate, sho ws a 90% correlati on in outcomes, suggest ing a high le v el of accurac y in object detection. The video injection method, with moderat e s ynchronization, sho ws a 75% correlation, indicating moderate accurac y . In contrast, real v ehicle testing, with the lo west synchronization rate, has only a 60% correlation, reecting the greatest chance of inaccuracies in detection. T able 6 indicates the percentage of correlated outcomes for each method under dif ferent weather conditions. The projection method consistently sho ws the highest percentage of correlated outcomes, indicating its superior accurac y across all weather conditions. The video injection method demonstrates moderate accurac y , with its ef fect i v eness slightly diminishing in less f a v orable weather conditions lik e foggy night and night. The real v ehicle testing method has the lo west correlated outcomes, especially in challenging weather conditions, reecting the impact of en- vironmental f actors on object detection accurac y . T able 5. Comparati v e analysis of percentage of correlated outcomes of three methods Method Synchronization rate Correlated outcome (%) Projection method High (1-2 frames de viation) 90 V ideo injection method Moderate (3-4 frames de viation) 75 Real v ehicle testing Lo w (5 frames de viation) 60 T able 6. The correlated outcomes for object detection accurac y under six dif ferent weather conditions using three methods W eather condition Projection method (%) V ideo injection method (%) Real v ehicle testing (%) Day 92 80 70 F oggy day 88 75 65 Cloudy 90 78 68 Dusk 91 77 66 F oggy night 85 70 60 Night 83 68 58 Camer a-based advanced driver assistance with inte gr ated Y OLOv4 for r eal-time detection (K eerthi J ayan) Evaluation Warning : The document was created with Spire.PDF for Python.
2242 ISSN: 2252-8938 3.2.2. Computational complexity In terms of computational comple xity for object detection models under v arious weather conditions, the t hree methods e xhibit distinct characteristics. The projection method, typically the least comple x, main- tains a consistent computational load across dif ferent weather conditions, estimated at a comple xity le v el of around 30%. Its straightforw ard approach of capturing and processing real-w orld scenarios contrib utes to this consistenc y . The video injection method, with added comple xity due to the incorporation of synthetic video and en vironmental simulations, presents a moderate computational b urden, a v eraging about 50% across dif fer - ent weather conditions. This method’ s comple xity slightly escalates in adv erse weather conditions lik e foggy night, where additional processing is required. The real v ehicle testing method, ho we v er , f aces the highest computational challenges, a v eraging around 70% comple xity . This method’ s comple xity peaks in challenging weather scenarios such as foggy day and foggy night, where real-time processing of dynamic en vironmental and v ehicular data signicantly increases the computational load. In essence, the computational demand for each method v aries with the intricac y of the weather conditions, reecting the required data processing depth for accurate object detection in di v erse en vironmental scenarios. 4. DISCUSSION In a comparati v e analysis of the three methods for object detection - projection method, video injec- tion method, and real v ehicle testing - notable dif ferences emer ge in terms of indi vidual object class detection, synchronization rate, correlated outcome percentage, and computational comple xity . Figure 3 s ho ws compar - ati v e analysis of object detection model testing methods. F or indi vidual object class detection, the projection method typical ly sho ws the highest accurac y with minimal misclassication, while the real v ehi cle testing method, dealing with dynamic real-w orld scenarios, re gisters a higher rate of misclassication. Synchroniza- tion rates, indicati v e of the methods’ alignment with real-time processing, are highest for the projection method (1-2 frames de viation), moderate for the video injection method (3-4 frames de viation), and lo west for real v e- hicle testing (5 frames de viation). These rates directly af fect the percentage of correlated outcomes, with the projection method achie ving about 90%, the video injection method around 75%, and real v ehicle testing approximately 60%. Computational comple xity follo ws a similar trend; the projection method is the least com- ple x at around 30%, the video injection method stands at 50%, and real v ehicle testing is the most comple x, a v eraging 70%. This consolidated vie w highlights the trade-of fs between these methods in terms of accurac y , real-time dat a processing capabilities , and computational demands, underlining the challenges in optimizing object detection models for adv anced dri v er -assistance systems. Figure 3. Comparati v e analysis of object detection model testing methods T able 7 compares three models for traf c sign recognition in terms of their algorithms, dataset, accu- rac y , computational ef cienc y , and synchronization rate. The proposed model, which utilizes Y olo v4 with VFF , achie v es the highest accurac y a t 96.5% on the GTSRB dataset, slightly surpassing the model in [33], which reaches 96% on the same dataset. Additionally , the proposed model demonstrates e xceptional computational ef cienc y , operating at 30 frames per second (fps), which is signicantly f aster than Gunasekara et al . [33] Int J Artif Intell, V ol. 14, No. 3, June 2025: 2236–2245 Evaluation Warning : The document was created with Spire.PDF for Python.
Int J Artif Intell ISSN: 2252-8938 2243 model (4.5 fps) and Santos et al . [34] model (8 fps). This ef cienc y mak es it more suitable for real-time applications. Furthermore, the proposed model has a lo wer synchronization rate (5), indicating potentially reduced processing delays compared to the other models, where Gunasekara et al . [33] m odel has a rate of 10 and Santos et al . [34] model has a rate of 8. A graphical representation of this comparison is pro vided in Figure 4, where our model demonstrates clear super iority across all performance metrics compared to the other tw o models. Ov erall, the proposed model outperforms the others in both ac curac y and speed, making it an optimal choice for real-time traf c sign recognition tasks. T able 7. Comparison of proposed model with baseline models Model Algorithm used Dataset Accurac y (%) Computational ef cienc y (fps) Synchronization rate Gunasekara et al. [33] Y OLO + Xception GTSRB 96 4.5 10 Santos et al. [34] CNN Napier Uni v ersity traf c dataset 92.97 8 8 Proposed model Y olo v4 + VFF GTSRB 96.5 30 5 Figure 4. Comparison of proposed model with baseline models 5. CONCLUSION This research has successfully demonstrated a comprehensi v e analysis of object detection in AD AS using three distinct methods: the projection method, video injection method, and real v ehicle testing. Our ndings re v eal signicant v ariations in performance metrics such as indi vidual object class detection, syn- chronization rate, percentage of correlated outcome, and computational comple xity acr o s s dif ferent weather conditions. The projection method, with its high synchronization rate and lo wer computational comple xity , consistently sho wed the highest accurac y in object class detection, particularly in standard weather conditions. This method pro v ed to be rob ust in terms of correlated outcomes, achie ving the highest percentage of accurac y across v arious scenarios. In contrast, the video injection method, while moderately comple x, e xhibited a bal- anced performance in terms of synchronization and object detection accurac y . This method w as particularly ef fecti v e in moderately challenging weather conditions, of fering a viable alternati v e for en vironments where realtime data is not critical. The real v ehicle testing approach, despite its higher computational demand and lo wer synchroni zation rate, pro vided in v aluable insights into the performance of AD AS under realistic and dynamically changing en vironmental conditions. Although it recorded a higher rate of misclassication, this method’ s real-w orld applicability is undeniable, especially for testing in adv erse weather conditions. Across all methods, weather conditions lik e foggy nights and hea vy rain posed s ignicant challenges, af fecting the accurac y and reliability of object detection. These ndings underscore the need for further research and de v el- opment in AD AS technology , particularly in enhancing object detection algorithms to cope with di v erse and challenging en vironmental f actors. Ov erall, this research contrib utes signicantly to the eld of autonomous v ehicle technology , of fering critical insights into the strengths and limitations of v ari o us object detection meth- ods. It lays the groundw ork for future adv ancements i n AD AS, pa ving the w ay for more rob ust, reliable, and safe autonomous dri ving solutions. Camer a-based advanced driver assistance with inte gr ated Y OLOv4 for r eal-time detection (K eerthi J ayan) Evaluation Warning : The document was created with Spire.PDF for Python.
2244 ISSN: 2252-8938 FUNDING INFORMA TION Authors state no funding in v olv ed. A UTHOR CONTRIB UTIONS ST A TEMENT This journal uses the C o nt rib utor Roles T axonomy (CRediT) to recognize indi vidual author contrib u- tions, reduce authorship disputes, and f acilitate collaboration. Name of A uthor C M So V a F o I R D O E V i Su P Fu K eerthi Jayan Balakrishnan Murug anantham C : C onceptualization I : I n v estig ation V i : V i sualization M : M ethodology R : R esources Su : Su pervision So : So ftw are D : D ata Curation P : P roject Administration V a : V a lidation O : Writing - O riginal Draft Fu : Fu nding Acquisition F o : F o rmal Analysis E : Writing - Re vie w & E diting CONFLICT OF INTEREST ST A TEMENT Authors state no conict of interest. D A T A A V AILABILITY Data a v ailability is not applicable to this paper as no ne w data were created or analyzed in this study . REFERENCES [1] K. Jayan and B. Murug anant ham, Adv anced dri v er assistance system technologies and its challenges to w ard the de v elopment of autonomous v ehicle, 5th International Conference on Intelligent Computing and Applications (ICICA 2019), 2021, v ol. 1172, pp. 55–72, doi: 10.1007/978-981-15-5566-4 6. [2] V . W . Saputra, N. Suciati, and C. F atichah, “F og and rain augmentation for license plate recognition in tropical country en vironment, IAES International Journal of Articial Intelligence, v ol. 13, no. 4, pp. 3951-3961, 2024, doi: 10.11591/ijai.v13.i4.pp3951-3961. [3] E. O. Appiah and S. Mensah, “Object detection in adv erse weather condition for autonomous v ehicles, Multimedia T ools and Applications , v ol. 83, no. 9, pp. 28235–28261, 2024, doi: 10.1007/s11042-023-16453-z. [4] T . Sharma, B. Debaque, N. Duclos, A. Chehri, B. Kinder , and P . F ortier , “Deep learning-based object detection and scene perception under bad weather conditions, Electronics, v ol. 11, no. 4, Feb . 2022, doi: 10.3390/electronics11040563. [5] H. J. V ishnukumar , B. Butting, C. M ¨ uller , and E. Sax, “Machine learning and deep neural netw ork Articial intelligence core for l ab and real-w orld test and v alidation for AD AS and autonomous v ehicles: AI for ef cient and quality test and v alidation, 2017 Intelligent Systems Conference (IntelliSys), 2017, pp. 714-721, doi: 10.1109/IntelliSys.2017.8324372. [6] M . Mostaf a and M. Ghantous, A Y OLO based approach for traf c light recognition for AD AS systems, 2022 2nd International Mo- bile, Intelligent, and Ubiquitous Computing Conference (MIUCC), 2022, pp. 225-229, doi: 10.1109/MIUCC55081.2022.9781682. [7] L. Masel lo, B. Sheehan, F . Murph y , G. Castignani, K. McDonnell, and C. Ryan, “From traditional to autonomous v ehicles: A systematic re vie w of data a v ailability , T ransportation Research Record: Journal of the T ransportation Research Board, v ol. 2676, no. 4, pp. 161–193, Dec. 2021, doi: 10.1177/03611981211057532. [8] “T axonomy and denitions for terms related to dri ving automation systems for on-road motor v ehicles - J3016 202104, SAE International , Apr . 2021. [Online]. A v ailable: https://www .sae.or g/standards/content/j3016 202104 [9] D. T abernik and D. Sk o ˇ caj, “Deep learning for lar ge-scale traf c-sign detection and recognition, IEEE T ransactions on Intelligent T ransportation Systems, v ol. 21, no. 4, pp. 1427-1440, May 2019, doi: 10.1109/TITS.2019.2913588. [10] E. G ¨ une y , C. Bayilmis ¸ , and B. C ¸ akan, An implementation of real-time traf c signs and road objects detect ion based on mobile GPU platforms, IEEE Access, v ol. 10, pp. 86191-86203, 2022, doi: 10.1109/A CCESS.2022.3198954. [11] Q. W ang, X. Li, and M. Lu, An impro v ed traf c sign detection and recognition deep model based on Y OLOv5, IEEE Access, v ol. 11, pp. 54679-54691, 2023, doi: 10.1109/A CCESS.2023.3281551. [12] S . Gautam and A. K umar , “Image-based automatic traf c light s detection system for autonomous cars: a re vie w , Multimedia T ools and Applications, v ol. 82, no. 17, pp. 26135–26182, Jan. 2023, doi: 10.1007/s11042-023-14340-1. [13] ´ A. Arcos-Garc ´ ıa, J. A. ´ Alv arez-Garc ´ ıa, and L. M. Soria-Morillo, “Ev aluation of deep neural netw orks for traf c sign detection systems, Neurocomputing, v ol. 316, pp. 332–344, Aug. 2018, doi: 10.1016/j.neucom.2018.08.009. [14] T . T ettamanti, M. Szalai, S. V ass, and V . T ihan yi, “V ehicle-in-the-loop test en vironment for autonomous dri ving with micro- scopic traf c simulation, 2018 IEEE International Conference on V ehicular Electronics and Safet y (ICVES), 2018, pp. 1-6, doi: 10.1109/ICVES.2018.8519486. [15] D. Bari ´ c, R. Grbi ´ c, M. Suboti ´ c, and V . Mihi ´ c, “T esting en vironment for AD AS softw are solutions, 2020 Zooming Inno v ation in Consumer T echnologies Conference (ZINC), 2020, pp. 190-194, doi: 10.1109/ZINC50678.2020.9161772. Int J Artif Intell, V ol. 14, No. 3, June 2025: 2236–2245 Evaluation Warning : The document was created with Spire.PDF for Python.
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Igel, “Detection of traf c signs in real-w orld images: The German traf c sign detection benchmark, The 2013 International Joint Conference on Neural Netw orks (IJCNN), 2013, pp. 1–8, doi: 10.1109/IJCNN.2013.6706807. [32] C . G. Serna and Y . Ruichek, “T ra f c signs detection and classicat ion for european urban en vironments, IEEE T ransactions on Intelligent T ransportation Systems, v ol. 21, no. 10, pp. 4388–4399, Oct. 2020, doi: 10.1109/tits.2019.2941081. [33] S. Gunasekara, D. Gunarathna, M. B. Dissanayak e, S. Aramith, and W . Muhammad, “Deep learning based autonomous real-time traf c sign recognition system for adv anced dri v er assistance, International Journal of Image Graphics and Signal Processing, v ol. 14, no. 6, pp. 70–83, Dec. 2022, doi: 10.5815/ijigsp.2022.06.06. [34] A . Sa ntos, P . A. Ab u, C. Oppus, and R. Re yes, “Real -time traf c sign detection and recognition system for assisti v e dri ving, Adv ances in Science T echnology and Engineering Systems Journal, v ol. 5, no. 4, pp. 600–611, Jan. 2020, doi: 10.25046/aj050471. BIOGRAPHIES OF A UTHORS K eerthi J ayan recei v ed the B.T ech. de gree in computer science and engineering from Am- rita V ishw a V idyapeetham, Amrita School of Engineering, K erala, India, in 2012 and the M.T ech. de gree in computer science and engineering from Amrita V ishw a V idyapeetham, Amrita School of Engineering, K erala, India, in 2014. Currently , she is pursuing a Ph.D. from the Department of Computing T echnologies, School of Computing, SRM Institute of Science and T echnology , Kat- tankulathur , T amil Nadu, India. Her research primarily centers on applying deep learning to the de v elopment of autonomous v ehicles. She can be contacted at email: kj4134@srmist.edu.in. Muruganantham Balakrishnan recei v ed the B.E. de gree in computer science and en- gineering from Manonmaniam Sundaranar Uni v ersity , T amil Nadu, India, in 1994, and the M.T ech. de gree in computer science and engineering from SRM Institute of Science and T echnology , T amil Nadu, India, in 2006, and the Ph.D. de gree in computer science and engineering from SRM Insti- tute of Science and T echnology , T amil Nadu, India, in 2018. He be g an his career in 1994 and has w ork ed in v arious industries. Currently , he is w orking as an Associate Professor in the Department of Computi ng T echnologies, School of Computing, SRM Institute of Science and T echnology , Kat- tankulathur , T amil Nadu, India. He can be contacted at email: murug anb@srmist.edu.in. Camer a-based advanced driver assistance with inte gr ated Y OLOv4 for r eal-time detection (K eerthi J ayan) Evaluation Warning : The document was created with Spire.PDF for Python.