Enhancing Smart City Safety: Deep Learning Approaches for Automatic Vehicle Accident Recognition

Traffic accidents have significant societal impacts due to the substantial human and material losses they cause. Recently, numerous AI-based traffic surveillance technologies, such as Saher, have been implemented to improve traffic safety in Saudi Arabia. The prompt detection of vehicle accidents is crucial for enhancing the response time of accident management systems, thereby reducing the number of injuries resulting from collisions. This study evaluates various deep learning algorithms to determine the most effective method for detecting and classifying car accidents. Multiple deep-learning models were trained and tested using an extensive dataset of car accident images, allowing for the accurate identification and classification of different types of accidents. Among the six pre-trained models analyzed, ResNet-101 achieved the highest accuracy, with a classification rate of 93%. For accident detection, YOLOv5 attained a mean Average Precision (mAP) of 97.8%, indicating superior performance compared to YOLOv8 and YOLOv9, and highlighting its capability to effectively detect accidents in video footage. The research’s primary goal is to enhance urban safety by enabling rapid accident detection, which supports timely emergency responses, minimizes fatalities, and contributes to the development of safer and more resilient smart cities.

@article{AlNemari2025,
  title     = {Enhancing Smart City Safety: Deep Learning Approaches for Automatic Vehicle Accident Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ahad AlNemari and Shahad AlOtaibi and Majd Jada and Aeshah AlHarthi and Sara AlThuwaybi and Wojoud AlNemari and Nadan Marran and Abdulmajeed Alsufyani},
  doi       = {10.14569/IJACSA.2025.0160933},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160933}
}