Single object detection to support requirements modeling using faster R-CNN

Abstract

Requirements engineering (RE) is one of the most important phases of a software engineering project in which the foundation of a software product is laid, objectives and assumptions, functional and non-functional needs are analyzed and consolidated. Many modeling notations and tools are developed to model the information gathered in the RE process, one popular framework is the iStar 2.0. Despite the frameworks and notations that are introduced, many engineers still find that drawing the diagrams is easier done manually by hand. Problem arises when the corresponding diagram needs to be updated as requirements evolve. This research aims to kickstart the development of a modeling tool using Faster Region-based Convolutional Neural Network for single object detection and recognition of hand-drawn iStar 2.0 objects, Gleam grayscale, and Salt and Pepper noise to digitalize hand-drawn diagrams. The single object detection and recognition tool is evaluated and displays promising results of an overall accuracy and precision of 95%, 100% for recall, and 97.2% for the F-1 score.