Fighting against terrorism: A real-time CCTV autonomous weapons detection based on improved YOLO v4

Abstract

In recent years, deep learning has demonstrated tremendous potential in the real world. Object detection is a critical real-world task for deep learning. You Only Look Once (YOLO) object detection model recognizes interesting regions in images with impressive accuracy and real-time performance. The objective of this paper is to apply object detection to the field of security and counter-terrorism. Individuals are protected from violence by recognizing and locating the guns on closed-circuit television (CCTV). This paper presents a real-time detection approach for CCTV autonomous weapons based on YOLO v4. For the characteristics of CCTV scenarios, we propose the YOLO v4 backbone with Spatial Cross Stage Partial-ResNet (SCSP-ResNet). Meanwhile, the receptive field enhancement module is shown to capture fine semantic features of high-dimensional small objects. The Fusion-PANet (F-PaNet) module has been used to fuse multi-scale information to improve the model's perceptive power on the region of interest. Furthermore, we merge synthetic and real-world datasets to comprehensively investigate the effects of synthetic datasets on detectors. The experimental results reveal that our suggested detection model improves mAP (mean Accuracy Precision) and inference time by 7.37% and 4.2%, respectively. The model's parameter is reduced by 0.349 BFLOP/s(billion floating point operations per second). The proposed detector outperforms the baseline model in terms of accuracy, real-time, and robustness.