A Compact YOLOv5-GhostNet-based Weapon Detection System for Smart City Applications

Abstract

Generally, various handheld weapons, such as guns, swords, knives, etc., are used in criminal activities. Further, real-time detection of these weapons using intelligent video surveillance systems can act as a deterrent and legal evidence in smart city applications. Hence, this paper proposes a compact and e cient weapon detector based on the You Only Look Once (YOLOv5)-GhostNet model. A new weapon dataset, Weapon7, comprises seven weapon classes such as Axe, Bow and Arrow, Gun, Knife, Lathi, Pistol, and Sword, with proper annotation les have been developed. Experimental analysis shows that the proposed model performs better than the equivalent reported works in terms of online performance metrics such as precision, recall, mAP, FPS, and GFLOPS