{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T23:00:27Z","timestamp":1752102027760},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683683","type":"print"},{"value":"9781643683690","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,13]]},"abstract":"<jats:p>In this paper, aiming at the complex background and overlapping characteristics in X-ray images, we propose an unique spatial attention mechanism based on the feedback of high-level semantic feature to guide low-level semantic features, named Feedback Guidance Mechanism (FGM). In addition, in view of the high probability of miss of small prohibited items, a feature aggregation method based on the fusion of high and low-level features and dilated convolution is proposed, named Feature Aggregation Module (FAM). Then, we combine FGM and FAM into a lightweight model SSD and get a new Prohibited Items Detector (PIXDet). Our experiments indicate that PIXDet is more lightweight, but it can achieve 90.36% mAP on PIXray dataset, exceeding SSD by 1.0% mAP, outperforming some state-of-the-art methods, implying its potential applications in prohibited items detection field.<\/jats:p>","DOI":"10.3233\/faia220521","type":"book-chapter","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:58:43Z","timestamp":1671609523000},"source":"Crossref","is-referenced-by-count":4,"title":["PIXDet: Prohibited Items X-Ray Image Detection in Complex Background"],"prefix":"10.3233","author":[{"given":"Mingyuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichun","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Institute of Arts Science & Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Proceedings of CECNet 2022"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220521","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T07:58:44Z","timestamp":1671609524000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220521"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"ISBN":["9781643683683","9781643683690"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220521","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}