Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4410
Title: A Compact YOLOv5-GhostNet-based Weapon Detection System for Smart City Applications
Authors: Nayak, Rashmiranjan
Sahoo, Goutam Kumar
Pati, Umesh Chandra
Das, Santos Kumar
Singh, Poonam
Keywords: Object detection
Weapon detection
YOLO
IoT
Crime activities
Smart city
Issue Date: Feb-2024
Citation: National conference on Intelligent Systems, IoT, and Wireless Communication for the Society (IIWCS), National Institute of Technology Rourkela 16-17 February 2024
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
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/4410
Appears in Collections:Conference Papers

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