An Overview of Different Deep Learning Techniques Used in Road Accident Detection

Every year, numerous lives are tragically lost because of traffic accidents. While many factors may lead to these accidents, one of the most serious issues is the emergency services' delayed response. Often, valuable time is lost due to a lack of information or difficulty determining the location and severity of an accident. To solve this issue, extensive research has been conducted on the creation of effective traffic accident detection and information communication systems. These systems use new technology, such as deep learning algorithms, to spot accidents quickly and correctly and communicate important information to emergency workers. This study provides an overview of current research in this field and identifies similarities among various systems. Based on the review findings, it was found that researchers utilised various techniques, including MLP (Multilayer Perceptron), CNN (Convolutional Neural Network), and models such as DenseNet, Inception V3, LSTM (Long short-term memory), YOLO (You Only Look Once), and RNN (Recurrent Neural Network), among others. Among these models, the MLP model demonstrated high accuracy. However, the Inception V3 model outperformed the others in terms of prediction time, making it particularly well-suited for real-time deployment at the edge and providing end-to-end functionality. The insights gained from this review will help enhance systems for detecting traffic accidents, which will lead to safer roads and fewer casualties. Future research must address several challenges, despite the promising results showcased by the proposed systems. These challenges include low visibility during nighttime conditions, occlusions that hinder accurate detection, variations in traffic patterns, and the absence of comprehensive annotated datasets.