The Design and Implementation of a Weapon Detection System Based on the YOLOv5 Object Detection Algorithm

Abstract

In recent years, as the increases of people’s concerns on environmental and body safety, various image-based detection techniques and research have gained wide attention. Currently, object-detection algorithms can be generally divided into two categories: traditional ones which extract features manually, and deep learning-based approaches that automatically extract features from images. Since the former requires a lot of manpower, material resources, and financial costs and consumes a lot of time to screen abnormal images, it no longer meets the urgent needs by our societies. In other words, more intelligent identification systems are required.

For society security reason, if different types of weapons, such as sticks, knives, and guns, can be detected in surveillance images, this can effectively prevent the chance of gangsters carrying weapons and acting fiercely or seeking revenge. To identify weapons, we need to distinguish them from other surveillance objects and images in a real-time manner. But most cameras have limited computing power, and images captured in the real world have their own problems, such as noise, blur, and rotation jitter, which need to be solved if we want to correctly detect weapons. Therefore, in this study, we develop a weapon detection system for surveillance images by employing a deep learning model. The intelligent tool used for image detection is YOLO (You Only Look Once)-v5, a lightweighting architecture of YOLO series and Sohas (Small Objects Handled Similarly to a weapon) dataset are adopted for image detection comparison. According to our simulation results, we successfully reduced the number of parameters in the YOLOv5s model by substituting the backbone with Shufflenetv2, replacing the PANet upsample module in the neck with the CARAFE (Content-Aware ReAssembly of Features upsample) upsample module, and replacing the SPPF (Spatial Pyramid Pooling-Fast) module with three lightweight options of simp PPF. These changes resulted in a 16.35% reduction in the parameter size of the YOLOv5s model, a 30.38% increase in FLOPS computational efficiency, and a decrease of 0.024 in mAP@0.5.