International Journal of Computer Techniques Volume 12 Issue 4 | AI-Powered CCTV Surveillance with Intrusion Detection Using YOLOv5 and Raspberry Pi
AI-Powered CCTV Surveillance with Intrusion Detection Using YOLOv5 and Raspberry Pi
Author: Harsh
Department of Computer Applications, Degree College Bhoranj (HPU)
Email: harshbanyal001@gmail.com
Journal: International Journal of Computer Techniques – Volume 12 Issue 4
Publication Date: July – August 2025
ISSN: 2394-2231
Journal URL: https://ijctjournal.org/
Abstract
This paper presents a smart and cost-effective AI surveillance system for real-time intrusion detection using YOLOv5 on Raspberry Pi. By analyzing live CCTV feeds, the system identifies unauthorized human or vehicle movement and sends alerts with image logs. Designed for homes, farms, hostels, and small industries, the system operates with minimal infrastructure and human intervention.
Keywords
YOLOv5, Raspberry Pi, Object Detection, CCTV, Intrusion Detection, Surveillance, Deep Learning, Computer Vision
Conclusion
This study confirms the viability of an intelligent CCTV surveillance system powered by YOLOv5 and Raspberry Pi. The model delivers accurate, real-time intrusion alerts for rural and budget-limited environments, promoting safety through smart automation and low-cost hardware.
References
- Glenn Jocher et al. (2023). YOLOv5 Documentation.
- Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement.
- Raspbian Pi Docs – Camera & TensorFlow Setup.
- B. J. Kang, K. J. Kim, D. W. Kim (2019). Lightweight person detection system using YOLOv5. Int. Conf. on Embedded Vision.
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