Night time Surveillance in Communication Using YOLOv3

Tapas K. Madan Pramanik; Prakash Gajananrao Burade; Sanjeev Sharma

doi:10.32628/IJSRSET2411592

Authors

Tapas K. Madan Pramanik Department of Electrical & Electronics Engineering, Faculty of Engineering, Sandip University, Nashik, Maharashtra, India Author
Prakash Gajananrao Burade Department of Electrical & Electronics Engineering, Faculty of Engineering, Sandip University, Nashik, Maharashtra, India Author
Sanjeev Sharma Department of Electronic and Communication Engineering, New Horizon College of Engineering, Bangalore, Karnataka, India Author

DOI:

https://doi.org/10.32628/IJSRSET2411592

Keywords:

Mean Average Precision, YOLOv3, DL Models, Deep Learning, Single Shot Detector

Abstract

Nighttime surveillance presents significant challenges due to low visibility, varying lighting conditions, and interference from artificial light sources. Hence, this study proposed an effective object detection framework using the YOLOv3 model to enhance real-time monitoring in night surveillance applications, specifically within communication and security systems. YOLOv3's architecture, with its multi-scale detection and use of predefined anchor boxes, enables robust detection of objects under low-light environments and amidst light interference from vehicles and streetlights. The proposed system is tested on a night surveillance dataset, where it demonstrates high precision and speed in identifying objects, making it suitable for real-time applications. With Mean Average Precision (mAP) 87.9%, YOLOv3 effectively balances detection accuracy with inference time, ensuring minimal latency in live surveillance feeds. The results indicate that YOLOv3 outperforms traditional models such as Faster RCNN, particularly in detecting small objects under poor illumination. This approach offers a reliable solution for enhancing communication and security systems in nighttime surveillance scenarios.

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