{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:30:16Z","timestamp":1772040616325,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"People\u2019s Public Security University of China","award":["2023SYL07"],"award-info":[{"award-number":["2023SYL07"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Using X-ray imaging in security inspections is common for the detection of objects. X-ray security images have strong texture and RGB features as well as the characteristics of background clutter and object overlap, which makes X-ray imaging very different from other real-world imaging methods. To better detect prohibited items in security X-ray images with these characteristics, we propose EM-YOLOv7, which is composed of both an edge feature extractor (EFE) and a material feature extractor (MFE). We used the Soft-WIoU NMS method to solve the problem of object overlap. To better extract features, the attention mechanism CBAM was added to the backbone. According to the results of several experiments on the SIXray dataset, our EM-YOLOv7 method can better complete prohibited-item-detection tasks during security inspection with detection accuracy that is 4% and 0.9% higher than that of YOLOv5 and YOLOv7, respectively, and other SOTA models.<\/jats:p>","DOI":"10.3390\/s23208555","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T10:36:56Z","timestamp":1697625416000},"page":"8555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["EM-YOLO: An X-ray Prohibited-Item-Detection Method Based on Edge and Material Information Fusion"],"prefix":"10.3390","volume":"23","author":[{"given":"Bing","family":"Jing","sequence":"first","affiliation":[{"name":"School of Information and Network Security, People\u2019s Public Security University of China, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pianzhang","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Vehicle and Mobility, Tsinghua University, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhui","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information and Network Security, People\u2019s Public Security University of China, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108245","DOI":"10.1016\/j.patcog.2021.108245","article-title":"Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging","volume":"122","author":"Akcay","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Batchelor, B.G. 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