A Comparative Study of Deep Learning Architectures for Real-Time Object Detection in Smart Surveillance Systems
Year: 2025
Type: article
Abstract: Smart surveillance systems have increasingly leveraged deep learning for real-time object detection, enhancing public safety, traffic monitoring, and anomaly detection. This study compares leading deep learning architectures—YOLOv5, SSD, and Faster R-CNN—for their performance in smart surveillance contexts. The models are evaluated on standard metrics such as accuracy (mAP), speed (FPS), and computational efficiency using the COCO and custom surveillance datasets. Our findings indicate that while YOLOv5 excels in real-time performance, Faster R-CNN offers higher accuracy but lags in speed. SSD provides a balance between the two. This comparative analysis aims to guide the selection of appropriate architectures based on specific surveillance system requirements.
Author: Brajen Kumar Deka
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409567693