{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T01:24:05Z","timestamp":1787189045309,"version":"build-2736575974"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Aiming at the problems of high missed detection rate and unbalanced feature extraction caused by the coexistence of multi\u2010scale objects in complex traffic scenarios, this paper proposes an improved YOLO11 detection algorithm (EPLS\u2010YOLO) for autonomous driving. First, the KITTI dataset is reconstructed and expanded, which is uniformly categorised into six classes (Car, Van, Truck, Pedestrian, Cyclist, and Tram). Data augmentation strategies such as Mosaic and random transformations are further adopted to enhance sample diversity. Second, in the backbone network, the Efficient Multi\u2010Scale Attention (EMA) is embedded into the C3K2 module to construct the C3K2_EMA module, which strengthens the fine\u2010grained feature representation of small objects and occluded objects. A P2 high\u2010resolution detection branch is added to cover the scale of extremely small objects, and a Lightweight Shared Convolution Detection Head (LSCD) is designed to achieve efficient fusion of multi\u2010scale features from shallow and deep layers. Finally, the SlideLoss function is introduced to dynamically assign sample learning weights based on Intersection over Union (IoU), alleviating the problem of unbalanced training of multi\u2010scale samples. Experimental results show that the proposed EPLS\u2010YOLO achieves a precision (\n                    <jats:italic>P<\/jats:italic>\n                    ) of 94.5%, recall (\n                    <jats:italic>R<\/jats:italic>\n                    ) of 92.0%, and mean Average Precision at IoU\u2009=\u20090.5 (mAP50) of 95.7% on the KITTI dataset, which are 0.7, 3.0 and 1.6 percentage\u2010points higher than those of the original YOLO11, respectively. Notably, the detection performance for small objects is significantly improved. Moreover, the overall detection performance of EPLS\u2010YOLO outperforms that of mainstream object detection models such as RT\u2010DETR, YOLOv8, YOLOv10 and YOLOv12, which can meet the demand for accurate perception of full\u2010scale objects in autonomous driving.\n                  <\/jats:p>","DOI":"10.1111\/exsy.70347","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T01:27:10Z","timestamp":1786930030000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>EPLS<\/scp>\n                    \u2010\n                    <scp>YOLO<\/scp>\n                    : A Multi\u2010Scale Object Detection Method for Complex Traffic Scenarios"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4676-5723","authenticated-orcid":false,"given":"Tao","family":"Feng","sequence":"first","affiliation":[{"name":"Chery Automobile Co., Ltd  Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Chery Automobile Co., Ltd  Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenli","family":"Wang","sequence":"additional","affiliation":[{"name":"Chery Automobile Co., Ltd  Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiusheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Chery Automobile Co., Ltd  Wuhu Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3252021"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2018.09.002"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2972974"},{"key":"e_1_2_8_5_1","unstructured":"Ge Z. S.Liu F.Wang et\u00a0al.2021.\u201cYolox: Exceeding Yolo Series in 2021 \u201darXiv Preprint arXiv:2107.08430."},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","unstructured":"Hidayatullah P. N.Syakrani M. R.Sholahuddin et\u00a0al.2025.\u201cYOLOv8 to YOLO11: A Comprehensive Architecture In\u2010Depth Comparative Review \u201darXiv Preprint arXiv:2501.13400.","DOI":"10.29207\/resti.v10i2.6598"},{"key":"e_1_2_8_8_1","first-page":"13713","article-title":"Coordinate Attention for Efficient Mobile Network Design","author":"Hou Q.","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3292278"},{"key":"e_1_2_8_11_1","unstructured":"Li C. L.Li H.Jiang et\u00a0al.2022.\u201cYOLOv6: A Single\u2010Stage Object Detection Framework for Industrial Applications \u201darXiv Preprint arXiv:2209.02976."},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.3071845"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.54254\/2755-2721\/67\/2024MA0067"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2023.03.009"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121036"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01790-1"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MPOT.2019.2939376"},{"key":"e_1_2_8_18_1","first-page":"1","volume-title":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Ouyang D.","year":"2023"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126627"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3364681"},{"key":"e_1_2_8_21_1","first-page":"618","article-title":"Grad\u2010Cam: Visual Explanations From Deep Networks via Gradient\u2010Based Localization","author":"Selvaraju R. 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