{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:18Z","timestamp":1753881558224,"version":"3.41.2"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"name":"Open Project Program of the State Key Lab of CAD&CG Zhejiang University","award":["A2117"],"award-info":[{"award-number":["A2117"]}]},{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province","award":["MSSB-2021-06"],"award-info":[{"award-number":["MSSB-2021-06"]}]},{"name":"Science&Technology Bureau of ChengDu","award":["2020-YF09-00005-SN"],"award-info":[{"award-number":["2020-YF09-00005-SN"]}]},{"name":"The Talent Cultivation Quality and Teaching Reform Project of Chengdu University","award":["cdjgb2022051"],"award-info":[{"award-number":["cdjgb2022051"]}]},{"name":"Project of Sichuan Research Center for Application and Development of Educational Informatization in 2021","award":["JYXX21-004"],"award-info":[{"award-number":["JYXX21-004"]}]},{"name":"Project of Urban and Rural Education Development Research Center in 2021","award":["TCCXJY-2021-E46"],"award-info":[{"award-number":["TCCXJY-2021-E46"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> Real-time and fast recognition of all kinds of traffic participants in intelligent driving has always been a major difficulty in the research of internet of vehicles. With the advent of edge computing, we try to deploy an image recognition algorithm directly to the intelligent vehicles. However, the original image recognition algorithm is difficult to be directly deployed on the vehicles due to limited edge device resources. Based on this, a fast recognition model of vulnerable traffic participants based on depthwise separable convolutional neural network (DSCYOLO) is proposed in this paper. The algorithm can significantly reduce the convolutional parameter quantity and computing load, making it suitable for deployment on the vehicle-mounted edge embedded devices. In order to validate the effectiveness of the proposed method, its simulation results are compared with the main target detection models Faster R-CNN, SSD and YOLOv3. The results show that the recognition time of the proposed model is reduced by 80.28%, 66.80% and 86.74%, respectively, on the basis of a relatively high recognition precision. The model can realize real-time detection and fast recognition of vulnerable traffic participants, so as to avoid a large number of traffic accidents. It has significant social and economic benefits. <\/jats:p>","DOI":"10.1142\/s0218126623500469","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T03:56:15Z","timestamp":1660881375000},"source":"Crossref","is-referenced-by-count":3,"title":["Research on Fast Recognition of Vulnerable Traffic Participants in Intelligent Connected Vehicles on Edge Computing"],"prefix":"10.1142","volume":"32","author":[{"given":"Musong","family":"Gu","sequence":"first","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu, P. R. China"},{"name":"Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu University, Chengdu, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4420-6521","authenticated-orcid":false,"given":"Jingjing","family":"Lyu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu, P. R. China"}]},{"given":"Zhongwen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu, P. R. China"}]},{"given":"Zihan","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Southern University of Science and Technology, Shenzhen, P. R. China"}]},{"given":"Wenjie","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chengdu University, Chengdu, P. R. 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Veh. Technol."},{"journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.","year":"2021","author":"Jiang W.","key":"S0218126623500469BIB011"},{"key":"S0218126623500469BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"first-page":"1440","volume-title":"Proc. IEEE Int. Conf. Computer Vision","author":"Girshick R.","key":"S0218126623500469BIB013"},{"key":"S0218126623500469BIB014","first-page":"91","volume-title":"Proc. Advances in Neural Information Processing Systems","author":"Ren S.","year":"2015"},{"key":"S0218126623500469BIB015","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"S0218126623500469BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"S0218126623500469BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3155112"},{"key":"S0218126623500469BIB019","first-page":"1","volume-title":"2017 IEEE Wireless Communications and Networking Conf. 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