{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:13:50Z","timestamp":1790093630997,"version":"4.0.1"},"reference-count":25,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T00:00:00Z","timestamp":1746835200000},"content-version":"vor","delay-in-days":129,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003482","name":"Department of Education of Hebei Province","doi-asserted-by":"publisher","award":["ZC2023011"],"award-info":[{"award-number":["ZC2023011"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Computers &amp; Digital Techniques"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    This study presents an intelligent moving target to replicate mob attacks and other realistic events in police training to match actual fighting needs. The police intelligent moving target must deploy target detection algorithms on the hardware platform, but the traditional you only look once (YOLO)v8 algorithm has a large framework, which will slow recognition due to the hardware platform\u2019s lack of arithmetic power. In this study, GhostNet network architecture replaces YOLOv8\n                    <jats:sup>\u2032<\/jats:sup>\n                    s backbone network for real\u2010time target identification, improving recognition speed. The bounding box regression issue in target detection uses the scale invariant intersection over union (SIoU) loss function to increase prediction box overlapping and identification accuracy. Finally, BiFormer uses dynamic sparse attention for more flexible computational allocation and content perception. The method\u2019s real\u2010time detection speed is 4.81 frames per second (FPS) faster, mAP@0.5 is 5.38% faster, mean average precision (mAP)@0.5:0.95 is 4.19% faster, and parameter volume is 5.81\u2009M less than the original approach. The approach developed in this work has several applications in real\u2010time target identification and lightweight deployment.\n                  <\/jats:p>","DOI":"10.1049\/cdt2\/9984821","type":"journal-article","created":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T07:50:44Z","timestamp":1746863444000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Application of Lightweight Target Detection Algorithm Based on YOLOv8 for Police Intelligent Moving Targets"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9493-2068","authenticated-orcid":false,"given":"Yanjie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1368-180X","authenticated-orcid":false,"given":"Xiaojun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuehan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zecong","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"265","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"key":"e_1_2_13_1_2","first-page":"82","article-title":"Research on Multimedia Simulation System for Military Training","volume":"24","author":"Juntao Y.","year":"2002","journal-title":"System Engineering and Electronic Technology"},{"key":"e_1_2_13_2_2","first-page":"955","volume-title":"Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)","author":"Cao J.","year":"2021"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8090959"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1714448"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101670"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_2_13_7_2","unstructured":"DaiJ. 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