ItemConference Paper

AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

Title
AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques
Creator
Karthikeyan, M.; Kshirsagar, Pravin Ramdas; Tak, Tan Kuan; Lalit, Keshav; Upreti, Kamal; Jain, Rituraj
Description
Computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection. 2025 IEEE.
Source
2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.1351-1356
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Accident Detection; Computer Vision; Fast SPPF; Fine Tune Augment YOLOv10; PSA
Coverage
Karthikeyan M., School of Computing, Srm Institute of Science and Technology, Dept. of Computing Technologies, Chennai, India; Kshirsagar P.R., J.D College of Engg & Mgmt., Department of Electronics Engg., MS, Nagpur, India; Tak T.K., Singapore Institute of Technology, Singapore; Lalit K., Manipal Institute of Technology, Dept. of Computer Science & Engineering, Karnataka, Manipal, India; Upreti K., Christ University, Department of Computer Science, Delhi NCR, Ghaziabad, India; Jain R., Marwadi University, Department of Info. Tech., Gujarat, Rajkot, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152749-5;
Format
online
Language
English
Type
Conference paper

Citation

Karthikeyan, M.; Kshirsagar, Pravin Ramdas; Tak, Tan Kuan; Lalit, Keshav; Upreti, Kamal; Jain, Rituraj, “AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25869.

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