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Advancements in computer vision and deep learning have led to significant progress in automated crime detection systems.This study focuses on the development of a novel mathematical model for crime detection based on the You Only Look Once (YOLOv5) network architecture.The proposed model utilizes state-of-the-art object detection techniques to identify, classify, and detect criminal activities in surveillance footage, including images and videos, focusing on critical crime categories such as weapons and violent behaviour.The model's performance is evaluated on seven classes of weapon objects and violent scenes, achieving a precision (P) of 0.842, recall (R) of 0.77, and mAP of 0.811.These results demonstrate the model's efficiency in accurately identifying and categorizing criminal activities, thereby contributing to enhancing public safety and security through the utilization of cutting-edge deep learning technologies in crime prevention and detection.
Apene et al. (Fri,) studied this question.