Advancing Real-Time Crime Weapon Detection and High-Risk Person Classification in Pre-Crime Scenes: A Comprehensive Machine Vision Approach Utilizing SSD Detector
The application of state-of-the-art in deep learning detection algorithms, such as You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD), presents a significant opportunity for enhancing crime prevention and control strategies. This research focuses on leveraging the SSD algorithm to detect common crime weapons on individuals in both pre-crime video scenes and real-world crime scenarios. By thoroughly understanding the operational principles of the SSD algorithm, we adapted it for the identification of dangerous weapons commonly associated with violent crimes. Our detection model, which targets both weapons and individuals, establishes a robust foundation for an artificial intelligence (AI) system that accurately predicts individuals at high risk. The model first identifies the presence of a person and subsequently checks for any of the specified weapons. If a weapon is detected, the system further analyzes the individual’s movement and speed within the frame of reference. Should the individual exceed a predetermined movement threshold, the system flags them as high risk. For this study, the SSD model utilized a VGG16 backbone and was trained on a dataset comprising 3,317 images, featuring four distinct weapon categories: handgun, shotgun, rifle, and knife. The dataset was collected from UCF, via Kaggle and complimented with additional weapons from Google download all, all the sources are secondary, open sources and loyalty-free. We achieved a mean average precision of 84.19% across five classes after training for 59 epochs The findings of this research demonstrate the effectiveness of the SSD algorithm in crime prevention and control, contributing to the ongoing discourse surrounding the application of detection algorithms for crime prediction. This work aims to provide technological innovations that can assist local law enforcement agencies in their operational duties. Additionally, the insights gained from this study may enhance the detection of abnormal behavior within the broader field of artificial intelligence.
