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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Dinamika Hukum Journal of Islamic Family Law
Kaur, Gagandeep
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Computer Science & IT Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
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.
Comparative analysis of Haar Cascade, OpenCV, and you only look once algorithms for vehicle detection Kaur, Gagandeep; Pawar, Shital; Patil, Rutuja Rajendra; Patil, Amol Vijay; Yenkikar, Anuradha V.; Bhandari, Nikita; Kadam, Kalyani Dhananjay
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10554

Abstract

Object detection is one of the substantial tasks in computer vision and has a wide range of applications ranging from autonomous driving to monitoring systems. This study presents a comparative analysis of vehicle detection approaches, contrasting traditional methods (OpenCV contour analysis and Haar Cascade) with modern deep learning-based you only look once version 8 (YOLOv8) and its variants. Vehicles were identified and localized within video frames using bounding boxes, with performance assessed through accuracy, F1-score, mean average precision (mAP), and inference speed. YOLOv8 consistently achieved superior accuracy (up to 98% in specific scenarios) and real-time processing speeds (155 FPS), confirming its suitability for safety-critical applications such as intelligent transport systems and autonomous navigation. However, its higher computational and memory demands highlight deployment trade-offs, where lighter variants like YOLOv8s remain feasible for embedded or low-power devices. In contrast, Haar Cascade and contour analysis offered faster execution and smaller memory footprints but lacked robustness under complex environmental conditions. The study also acknowledges limitations such as dataset bias, adverse weather effects, and scalability challenges, which may impact generalization in real-world deployments. By analyzing these trade-offs, the work provides essential insights to guide practitioners in selecting suitable vehicle detection solutions across diverse application environments.
Co-Authors Agrawal, Poorva Bhandari, Nikita Bobhate, Grishma Chhabada, Dev Gote, Anirudh Gupta, Chander Mohan Kadam, Kalyani Dhananjay Mehta, Pradnya Mota, Riya Patel, Dhimahi Patil, Amol Vijay Patil, Rutuja Rajendra Pawar, Shital Rahul Shinde, Gitanjali Rajendra Patil, Rutuja Safii, Nur Muhammad Sharma, Amit Wankhade, Renuka Yenkikar, Anuradha Yenkikar, Anuradha V. Yuliatiningsih, Aryuni