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TSGC0288"],"award-info":[{"award-number":["2023 TSGC0288"]}]},{"name":"Ministry of industry and information technology manufacturing high quality development project, China","award":["202333067"],"award-info":[{"award-number":["202333067"]}]},{"name":"Ministry of industry and information technology manufacturing high quality development project, China","award":["2019KJB019"],"award-info":[{"award-number":["2019KJB019"]}]},{"name":"Ministry of industry and information technology manufacturing high quality development project, China","award":["2022CXGC020706"],"award-info":[{"award-number":["2022CXGC020706"]}]},{"name":"Ministry of industry and information technology manufacturing high quality development project, China","award":["2023ZY02002"],"award-info":[{"award-number":["2023ZY02002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the issues of false detections and missed detections in object detection for intelligent driving scenarios, this study focuses on optimizing the YOLOv10 algorithm to reduce model complexity while enhancing detection accuracy. The method involves three key improvements. First, it involves the design of multi-scale flexible convolution (MSFC), which can capture multi-scale information simultaneously, thereby reducing network stacking and computational load. Second, it reconstructs the neck network structure by incorporating Shallow Auxiliary Fusion (SAF) and Advanced Auxiliary Fusion (AAF), enabling better capture of multi-scale features of objects. Third, it improves the detection head through the combination of multi-scale convolution and channel adaptive attention mechanism, enhancing the diversity and accuracy of feature extraction. Results show that the improved YOLOv10 model has a size of 13.4 MB, meaning a reduction of 11.8%, and that the detection accuracy mAP@0.5 reaches 93.0%, outperforming mainstream models in comprehensive performance. This work provides a detection framework for intelligent driving scenarios, balancing accuracy and model size.<\/jats:p>","DOI":"10.3390\/s25165084","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T14:24:28Z","timestamp":1755267868000},"page":"5084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Intelligent Vehicle Target Detection Algorithm Based on Multiscale Features"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9313-6107","authenticated-orcid":false,"given":"Aijuan","family":"Li","sequence":"first","affiliation":[{"name":"School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangsen","family":"Ning","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e1t\u00e9","family":"Z\u00f6ldy","sequence":"additional","affiliation":[{"name":"Department of Environmental Economics and Sustainability, Faculty of Economics and Social Sciences, Budapest University of Technology and Economics, M\u0171egyetem rkp 3-5, H-1111 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangpeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","first-page":"59","article-title":"Night driving target detection algorithm based on YOLOv8n improvement study","volume":"52","author":"Lu","year":"2025","journal-title":"J. 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