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However, traditional road map production methods mainly rely on professional survey technologies, such as remote sensing and mobile mapping, which suffer from high costs, object occlusions, and long updating cycles. In the era of ubiquitous mapping, crowdsourced trajectory data offer a new and low-cost data resource for the production and updating of high-precision road maps. Meanwhile, as key nodes in the transportation network, maintaining the currency and integrity of road intersection data is the primary task in enhancing map updates. In this paper, we propose a novel approach for detecting road intersections based on crowdsourced trajectory data by introducing an attention mechanism and modifying the loss function in the YOLOv5 model. The proposed method encompasses two key steps of training data preparation and improved YOLOv5s model construction. Multi-scale training processing is first adopted to prepare a rich and diverse sample dataset, including various kinds and different sizes of road intersections. Particularly to enhance the model\u2019s detection performance, we inserted convolutional attention mechanism modules into the original YOLOv5 and integrated other alternative confidence loss functions and localization loss functions. The experimental results demonstrate that the improved YOLOv5 model achieves detection accuracy, precision, and recall rates as high as 97.46%, 99.57%, and 97.87%, respectively, outperforming other object detection models.<\/jats:p>","DOI":"10.3390\/ijgi13060176","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T13:32:55Z","timestamp":1716903175000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Detecting Road Intersections from Crowdsourced Trajectory Data Based on Improved YOLOv5 Model"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-4940","authenticated-orcid":false,"given":"Yunfei","family":"Zhang","sequence":"first","affiliation":[{"name":"National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment (Changsha), Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2158-7721","authenticated-orcid":false,"given":"Gengbiao","family":"Tang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment (Changsha), Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naisi","family":"Sun","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment (Changsha), Changsha University of Science & Technology, Changsha 410114, China"},{"name":"School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"ref_1","unstructured":"Wang, C. 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