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Specifically, we propose a feature extraction module (FHR) with high-resolution representation, which efficiently handles multi-scale targets and high-resolution image information by efficiently fusing high-resolution information and multi-scale features. Secondly, we design a multi-scale feature extraction enhancement (MFE) module, which significantly expands the sensory field of the network, thus enhancing the ability to capture correlations between image details and global contextual information. In addition, we introduce a dual-attention mechanism module (CSD), which dynamically adjusts the network to more accurately capture subtle features and rich semantic information in images. We trained and evaluated HRDLNet on the Cityscapes Dataset and the PASCAL VOC 2012 Augmented Dataset, and verified the model\u2019s excellent performance in the field of urban streetscape image segmentation. The unique advantages of our proposed HRDLNet in the field of semantic segmentation of urban streetscapes are also verified by comparing it with the state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s40747-024-01582-1","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T12:03:52Z","timestamp":1722859432000},"page":"7825-7844","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HRDLNet: a semantic segmentation network with high resolution representation for urban street view images"],"prefix":"10.1007","volume":"10","author":[{"given":"Wenyi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Zongcheng","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Guokai","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"issue":"10","key":"1582_CR1","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.1007\/s11263-022-01657-x","volume":"130","author":"\u00c9 Zablocki","year":"2022","unstructured":"Zablocki \u00c9, Ben-Younes H, P\u00e9rez P et al (2022) Explain ability of deep vision-based autonomous driving systems: review and challenges. 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