Published Paper Details:

The rapid advancement of intelligent transportation systems (ITS) has necessitated the development of efficient and accurate methods for accident detection and impact evaluation. This review paper examines the latest image processing approaches utilized in the detection and analysis of traffic accidents. We explore a variety of techniques, including machine learning algorithms, deep learning models, and traditional computer vision methods, assessing their effectiveness in real-time accident detection, severity estimation, and post-accident analysis. The paper discusses the strengths and limitations of each approach, highlighting key innovations and their practical applications within ITS. Additionally, the review addresses the integration of image processing with other sensor technologies, such as LiDAR and radar, to enhance the reliability and accuracy of accident analysis. Current challenges, including data quality, computational demands, and implementation in diverse traffic environments, are also considered. Finally, we outline potential future research directions aimed at improving the robustness and scalability of image processing techniques for comprehensive accident impact evaluation. This review serves as a valuable resource for researchers and practitioners seeking to advance the field of traffic accident analysis through image processing innovations.