Comparative deep learning architectures for railway train detection using accelerometer
Telecommunication Computing Electronics and Control
Abstract
Railway level crossing accidents remain a critical safety concern worldwide, necessitating reliable train detection systems particularly at unguarded crossings. This study presents comparative evaluations from four deep learning (DL) architectures convolutional neural network (2D-CNN), long short-term memory (LSTM), MobileNetV2, and hybrid CNN-LSTM of railway train detection using triaxial accelerometer sensor data. Vibration signals acquired from piezoelectric sensors mounted on railway infrastructure were transformed into spectrogram representations and classified into four proximity categories: idle, far-distance (>400 m), near distance (200-400 m), and train passing (
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