{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:00:37Z","timestamp":1758704437090,"version":"3.37.3"},"reference-count":19,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T00:00:00Z","timestamp":1573430400000},"content-version":"unspecified","delay-in-days":0,"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":["41571401"],"award-info":[{"award-number":["41571401"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2019,11,11]]},"abstract":"<jats:p>The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16\u2009\u00d7\u200916, 32\u2009\u00d7\u200932, and 64\u2009\u00d7\u200964 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33\u2009fps) is fast, which can meet the real-time requirements of industry.<\/jats:p>","DOI":"10.1155\/2019\/3679203","type":"journal-article","created":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T18:32:33Z","timestamp":1573497153000},"page":"1-16","source":"Crossref","is-referenced-by-count":8,"title":["A Multibranch Object Detection Method for Traffic Scenes"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9155-1865","authenticated-orcid":true,"given":"Jiangfan","family":"Feng","sequence":"first","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Space Big Data Intelligent Technology Chongqing Engineering Research Center, School of Computer Science and Technology, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3798-9900","authenticated-orcid":true,"given":"Fanjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Space Big Data Intelligent Technology Chongqing Engineering Research Center, School of Computer Science and Technology, Chongqing 400065, China"}]},{"given":"Siqin","family":"Feng","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Space Big Data Intelligent Technology Chongqing Engineering Research Center, School of Computer Science and Technology, Chongqing 400065, China"}]},{"given":"Yongrong","family":"Peng","sequence":"additional","affiliation":[{"name":"Central South University, School of Computer Science and Technology, Changsha 410000, China"}]}],"member":"311","reference":[{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2018.2838132"},{"issue":"9","key":"8","first-page":"1904","volume":"37","year":"2014","journal-title":"IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2577031"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0620-5"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2018.2844175"},{"volume-title":"SSD: single shot multibox detector","year":"2016","key":"15"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/tec.2018.2849856"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2018.2845371"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/tvt.2010.2058134"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2013.2294646"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2006.877062"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2010.2040177"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16628-5_26"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.07.027"},{"issue":"1","key":"32","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TCSVT.2016.2598703","volume":"28","year":"2018","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2015.457"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2016.2572683"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/3679203.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/3679203.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2019\/3679203.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,11]],"date-time":"2019-11-11T18:32:35Z","timestamp":1573497155000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cin\/2019\/3679203\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,11]]},"references-count":19,"alternative-id":["3679203","3679203"],"URL":"https:\/\/doi.org\/10.1155\/2019\/3679203","relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"type":"print","value":"1687-5265"},{"type":"electronic","value":"1687-5273"}],"subject":[],"published":{"date-parts":[[2019,11,11]]}}}