{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:22:17Z","timestamp":1753885337614,"version":"3.41.2"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":90,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801018"],"award-info":[{"award-number":["61801018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["FRF-BD-19-002A"],"award-info":[{"award-number":["FRF-BD-19-002A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Campus security incidents occur from time to time, which seriously affect the public security. In recent years, the rapid development of artificial intelligence has brought technical support for campus intelligent security. In order to quickly recognize and locate dangerous targets on campus, an improved YOLOv3\u2010Tiny model is proposed for dangerous target detection. Since the biggest advantage of this model is that it can achieve higher precision with very fewer parameters than YOLOv3\u2010Tiny, it is one of the Tinier\u2010YOLO models. In this paper, the dangerous targets include dangerous objects and dangerous actions. The main contributions of this work include the following: firstly, the detection of dangerous objects and dangerous actions is integrated into one model, and the model can achieve higher accuracy with fewer parameters. Secondly, to solve the problem of insufficient YOLOv3\u2010Tiny target detection, a jump\u2010join repetitious learning (JRL) structure is proposed, combined with the spatial pyramid pooling (SPP), which serves as the new backbone network of YOLOv3\u2010Tiny and can accelerate the speed of feature extraction while integrating features of different scales. Finally, the soft\u2010NMS and DIoU\u2010NMS algorithm are combined to effectively reduce the missing detection when two targets are too close. Experimental tests on self\u2010made datasets of dangerous targets show that the average MAP value of the JRL\u2010YOLO algorithm is 85.03%, which increases by 3.22 percent compared with YOLOv3\u2010Tiny. On the VOC2007 dataset, the proposed method has a 9.29 percent increase in detection accuracy compared to that using YOLOv3\u2010Tiny and a 2.38 percent increase compared to that employing YOLOv4\u2010Tiny, respectively. These results all evidence the great improvement in detection accuracy brought by the proposed method. Moreover, when testing the dataset of dangerous targets, the model size of JRL\u2010YOLO is 5.84\u2009M, which is about one\u2010fifth of the size of YOLOv3\u2010Tiny (33.1\u2009M) and one\u2010third of the size of YOLOv4\u2010Tiny (22.4\u2009M), separately.<\/jats:p>","DOI":"10.1155\/2021\/5536152","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T21:50:09Z","timestamp":1617313809000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["JRL\u2010YOLO: A Novel Jump\u2010Join Repetitious Learning Structure for Real\u2010Time Dangerous Object Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9086-1784","authenticated-orcid":false,"given":"Yiliang","family":"Zeng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4746-0544","authenticated-orcid":false,"given":"Lihao","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4243-2277","authenticated-orcid":false,"given":"Jiahong","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jinhui","family":"Lan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0598-7441","authenticated-orcid":false,"given":"Biao","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijlp.2017.06.009"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/00091380903448997"},{"key":"e_1_2_9_3_2","article-title":"A campus climate\/violence survey\u2019s psychometrics and findings","author":"Follingstad D.","year":"2020","journal-title":"Violence against Women"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2020.2998818"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2016.12.023"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.487140"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.403798"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6625695"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2552206"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"XuX. 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