Design and Implementation of a Surveillance Security System Based on YOLO Algorithm and IoT Technology on Mobile Data Network

Subject Areas : Electronic Engineering

Mohamadreza Masaeli ¹ (Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Sayed Mohammadali Zanjani ² (Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2024-01-23 Accepted : 2024-04-22 Published : 2025-06-20

Keywords: Human activity recognition system (HAR), Machine vision, Violence prevention, Exporting and quantization, YOLO algorithm,

Abstract :

The design and implementation of a surveillance security system based on the YOLO algorithm and Internet of Things (IoT) technology has significant advantages in terms of security, efficiency, scalability, rapid response, and reliability, compared to traditional methods. This paper discusses the aspects of creating a novel security system that activates an alert by detecting five categories: human, human head, gun, knife, and fall detection. The system is monitored online and can connect to the internet via a cellular data network at any location to upload images to the management panel and send a report to the user if threats are detected. The YOLOv8 algorithm is used for object training to take advantage of its user-friendly command line interface, object detection support, sample segmentation, and image classification capabilities. To increase processing speed while maintaining accuracy, the optimized model is deployed on the Raspberry Pi 4th generation board. It is clear that optimizing processing speed and using quantization techniques lead to reduced energy consumption (green energy system) and reduced operational costs of the system. To improve the speed of the model in the object detection process, the techniques of exporting, quantizing the training weights, and increasing the processor frequency (overclocking) are used. A comparison of the newly exported weights with the original training weights in terms of accuracy and speed shows that the two techniques of exporting and quantization lead to an increase in processing speed at the cost of a decrease in detection accuracy.Finally, in the training model with the proposed improvement methods, an average accuracy of mAP ≅ 0.67 with a frame rate of FPS ≅ 4.3 can be achieved.

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