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All Journal Bulletin of Electrical Engineering and Informatics
Rahul Shinde, Gitanjali
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Electrical & Electronics Engineering

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Integration of deep learning algorithms for real-time vehicle accident detection from surveillance videos Mota, Riya; Wankhade, Renuka; Rahul Shinde, Gitanjali; Rajendra Patil, Rutuja; Bobhate, Grishma; Kaur, Gagandeep
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9587

Abstract

Major road accidents have increased due to the rapid rise of vehicles on the roads due to affordability and accessibility. While minor accidents can be resolved without the need for escorting to hospitals, significant accidents that involve the deployment of airbags necessitate the immediate attention of authorities. Thus, subsequent action of first aid and proper communication to concerned medical personnel can avoid most fatalities from accidents. The system involves the automatic detection of traffic accidents from videos extracted by closed-circuit television (CCTV) surveillance. In case of an accident, the system will detect and information about the accident will be instantly relayed to the nearest medical center. We have implemented different machine learning models such as Resnet-18, VGG-16, LeNet, and Inception V1 to ensure the accuracy of accident detection. From comparing all these models, the convolutional neural network (CNN) model shows the highest accuracy of 98%. The quick response will be an important step toward a safer and more secure transportation landscape.
Co-Authors Bobhate, Grishma Kaur, Gagandeep Mota, Riya Rajendra Patil, Rutuja Wankhade, Renuka