{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T21:58:18Z","timestamp":1775167098515,"version":"3.50.1"},"reference-count":42,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773106"],"award-info":[{"award-number":["61773106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JB2016GD034"],"award-info":[{"award-number":["JB2016GD034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Scientific and Technological Projects of the Ministry","award":["61773106"],"award-info":[{"award-number":["61773106"]}]},{"name":"Major Scientific and Technological Projects of the Ministry","award":["JB2016GD034"],"award-info":[{"award-number":["JB2016GD034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,1,18]]},"abstract":"<jats:p>For personal safety and crime prevention, some research studies based on deep learning have achieved success in the object detection of X-ray security inspection. However, the research on dangerous liquid detection is still scarce, and most research studies are focused on the detection of some prohibited and common items. In this paper, a lightweight dangerous liquid detection method based on the Depthwise Separable convolution for X-ray security inspection is proposed. Firstly, a dataset of seven common dangerous liquids with multiple postures in two detection environments is established. Secondly, we propose a novel detection framework using the dual-energy X-ray data instead of pseudocolor images as the objects to be detected, which improves the detection accuracy and realizes the parallel operation of detection and imaging. Thirdly, in order to ensure the detection accuracy and reduce the computational consumption and the number of parameters, based on the Depthwise Separable convolution and the Squeeze-and-Excitation block, a lightweight object location network and a lightweight dangerous liquid classification network are designed as the backbone networks of our method to achieve the location and classification of the dangerous liquids, respectively. Finally, a semiautomatic labeling method is proposed to improve the efficiency of data labeling. Compared with the existing methods, the experimental results demonstrate that our method has better performance and wider applicability.<\/jats:p>","DOI":"10.1155\/2022\/5371350","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T21:27:26Z","timestamp":1642541246000},"page":"1-14","source":"Crossref","is-referenced-by-count":4,"title":["A Lightweight Dangerous Liquid Detection Method Based on Depthwise Separable Convolution for X-Ray Security Inspection"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6382-3168","authenticated-orcid":true,"given":"Dongming","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"},{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2801-8312","authenticated-orcid":true,"given":"Jianchang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"},{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9687-8544","authenticated-orcid":true,"given":"Peixin","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2385-3180","authenticated-orcid":true,"given":"Feng","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"},{"name":"State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2018.2866413"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijthermalsci.2016.08.007"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2015.08.006"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2018.2822293"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/ccst.2007.4373490"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s10921-017-0419-3"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2015.2439233"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/s10921-016-0362-8"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32717-9_15"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.14257\/ijsia.2016.10.10.15"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.14355\/spr.2015.04.001"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/3679203"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2015.2437384"},{"key":"16","first-page":"1440","article-title":"Fast R-CNN","author":"R. 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