| Abstract |
Weapon detection is the process of identifying handheld weapons such as guns, knives, etc., and creating a bounding box around them to highlight the spatial locations. Weapon detection is one of the key building blocks of the intelligent video surveillance system for security applications in smart cities. However, detecting handheld weapons from surveillance videos is quite challenging due to small object size, occlusion, illumination variation, model complexity, and latency. Hence, an efficient, novel, robust, and lightweight YOLOv8-based weapon detector with GhostNet backbone and C3 module with transformer block (C3TR) neck (YOLO-GTWDNet model) is proposed for detecting the weapons either from stored images or from the live video streams. The proposed model is trained using a weapon dataset named "Weapon7," which is developed by collecting various weapon classes, such as Axe, Bow and arrow, Gun, Knife, Lathi, Pistol, and Sword, from various publicly available datasets, Internet, and own camera capture. Extensive experimental analysis is carried out to demonstrate the effectiveness of the proposed YOLO-GTWDNet model. The proposed model outperforms the state-of-the-art models when compared using both quantitative and qualitative performance metrics. The deployment of the proposed model is expected to bolster public safety significantly, providing city authorities with a powerful tool to mitigate risks and swiftly address potential threats. |