REAL-TIME WEAPON AND THREAT DETECTION USING YOLOV12 WITH MULTI-SENSOR FUSION FOR ENHANCED SURVEILLANCE SYSTEMS

Abstract

Deep learning-based object identification models have been incorporated for improved situational awareness and security monitoring as a result of the quick development of intelligent surveillance systems. In order to detect dangerous objects including guns, explosives, and suspicious goods in complicated situations, this study proposes an Advanced Surveillance Framework that makes use of YOLOv10, a next-generation real-time object detection algorithm. In order to increase detection accuracy in a variety of illumination and occlusion scenarios, the suggested system integrates visual and infrared modalities through multi-sensor data fusion. Through enhanced feature aggregation, adaptive anchor mechanisms, and transformer-based attention modules, YOLOv10's optimised architecture provides greater speed–accuracy trade-offs. The fusion-based YOLOv10 model is a potent solution for contemporary surveillance applications in public safety, border control, and smart city security networks because experimental results show that it greatly outperforms conventional single-sensor approaches in precision, recall, and real-time responsiveness.