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Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas.<\/jats:p>","DOI":"10.3390\/s24113588","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T05:58:00Z","timestamp":1717394280000},"page":"3588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance"],"prefix":"10.3390","volume":"24","author":[{"given":"Yu-Tong","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Yanbian University, Yanji 133002, China"}]},{"given":"Kai-Yang","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Yanbian University, Yanji 133002, China"}]},{"given":"De","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Yanbian University, Yanji 133002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1151-6814","authenticated-orcid":false,"given":"Jin-Chun","family":"Piao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Yanbian University, Yanji 133002, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108076","DOI":"10.1016\/j.engappai.2024.108076","article-title":"FDTNet: Enhancing frequency-aware representation for prohibited object detection from X-ray images via dual-stream transformers","volume":"133","author":"Zhu","year":"2024","journal-title":"Eng. 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