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To overcome these challenges, we introduce <jats:italic>SC\u2010YOLO<\/jats:italic>, a lightweight detection framework built upon YOLOv10n and optimized for greater efficiency. First, we replace the standard downsampling between the backbone and neck with a Space\u2010to\u2010Depth Convolution (SPDConv) module, preserving fine\u2010grained details in the lower levels of the feature pyramid so that cues for small targets remain intact. Next, we propose a Context\u2010Guided Rectangular Feature Pyramid Network (CGRFPN) equipped with a self\u2010calibration mechanism; by enabling cross\u2010scale interactions and adaptive feature\u2010map calibration, it significantly enhances multi\u2010scale fusion. Finally, guided by extensive empirical evaluation, we adopt the Wise\u2010IoUv3 dynamic loss function, whose adaptive gradient allocation refines bounding\u2010box regression. On the Pascal VOC, KITTI, and Cars datasets, SC\u2010YOLO attains mAP@50 scores of 79.0%, 87.3%, and 74.6%, respectively\u2014improving upon the YOLOv10n baseline by 2.5%, 2.1%, and 2.3%. Crucially, it maintains high accuracy under challenging traffic conditions, especially for small\u2010vehicle detection and occlusion resolution, while scaling more efficiently, requiring fewer computations than other models with comparable parameter counts. These combined advantages underscore SC\u2010YOLO's resource\u2010efficient design and its practicality for intelligent transportation and autonomous\u2010driving applications.<\/jats:p>","DOI":"10.1002\/cpe.70268","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:48:22Z","timestamp":1756946902000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>SC<\/scp>\u2010<scp>YOLO<\/scp>: Robust Multi\u2010Scale Small Object Detection for Intelligent Transportation"],"prefix":"10.1002","volume":"37","author":[{"given":"Keyou","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence Beijing Technology and Business University  Beijing China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5424-1079","authenticated-orcid":false,"given":"Jiangnan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence Beijing Technology and Business University  Beijing China"}]},{"given":"Haibing","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence Beijing Technology and Business University  Beijing China"}]},{"given":"Pei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence Beijing Technology and Business University  Beijing China"}]},{"given":"Huangcheng","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence Beijing Technology and Business University  Beijing China"}]}],"member":"311","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2024.104594"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/wevj15070323"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103649"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/ma17194679"},{"key":"e_1_2_6_6_1","doi-asserted-by":"crossref","unstructured":"R.Sapkota R.Qureshi M.Flores Calero et al. \u201cYOLOv12 to Its Genesis: A Decadal and Comprehensive Review of the You Only Look Once (YOLO) Series \u201d arXiv 2024 https:\/\/doi.org\/10.48550\/arXiv.2406.19407.","DOI":"10.36227\/techrxiv.171995313.38075268\/v1"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11554\u2010024\u201001517\u20106"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14041557"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3039574"},{"key":"e_1_2_6_10_1","first-page":"2980","volume-title":"16th IEEE Int Conf Comput Vis","author":"He K. 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