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http://dx.doi.org/10.3837/tiis.2022.08.012

SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm  

Kim, Eunchan (Department of Intelligence and Information, Seoul National University)
Lee, Jinyoung (Department of Artificial Intelligence, Yonsei University)
Jo, Hyunjik (Department of Artificial Intelligence, Yonsei University)
Na, Kwangtek (Department of Electrical and Computer Engineering, Inha University)
Moon, Eunsook (Department of Artificial Intelligence, Yonsei University)
Gweon, Gahgene (Department of Intelligence and Information, Seoul National University)
Yoo, Byungjoon (Department of Intelligence and Information, Seoul National University)
Kyung, Yeunwoong (School of Computer Engineering, Hanshin University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.8, 2022 , pp. 2688-2703 More about this Journal
Abstract
Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.
Keywords
Computer vision; detection of hazardous items; small-object detection; YOLO; air transport; security industries;
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