{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:19:27Z","timestamp":1773415167487,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["20231j0195"],"award-info":[{"award-number":["20231j0195"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes.<\/jats:p>","DOI":"10.3390\/s24072302","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T02:25:32Z","timestamp":1712283932000},"page":"2302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["PDT-YOLO: A Roadside Object-Detection Algorithm for Multiscale and Occluded Targets"],"prefix":"10.3390","volume":"24","author":[{"given":"Ruoying","family":"Liu","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China"}]},{"given":"Miaohua","family":"Huang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China"}]},{"given":"Liangzi","family":"Wang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China"}]},{"given":"Chengcheng","family":"Bi","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China"}]},{"given":"Ye","family":"Tao","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China"},{"name":"Hubei Research Center for New Energy & Intelligent Connected Vehicle, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yu, H., Luo, Y., Shu, M., Huo, Y., Yang, Z., Shi, Y., Guo, Z., Li, H., Hu, X., and Yuan, J. 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