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By incorporating multi-scale dilated convolution and adaptive spatial channel attention fusion modules, the MALNet model significantly enhances the precision, integrity, and robustness of its segmentation branch. Furthermore, it employs an intricate knowledge distillation strategy, channeling rich, layered insights from a teacher model to a student model, thus elevating the latter\u2019s segmentation ability. Concurrently, it streamlines the student model by markedly reducing its parameter count and computational demands, culminating in a segmentation network that is both high-performing and pragmatic. Rigorous testing on three distinct data sets for damaged road marking detection\u2014CDM_P (Collective Damaged road Marking\u2014Public), CDM_H (Collective Damaged road Marking\u2014Highways), and CDM_C (Collective Damaged road Marking\u2014Cityroad)\u2014underscores the MALNet model\u2019s superior segmentation abilities across all damage types, outperforming competing models in accuracy and completeness. Notably, the MALNet model excels in parameter efficiency, computational economy, and throughput. After distillation, the student model\u2019s parameters and computational load decrease to only 31.78% and 27.40% of the teacher model\u2019s, respectively, while processing speeds increase to 1.9 times, demonstrating a significant improvement in lightweight design.<\/jats:p>","DOI":"10.3390\/rs16142593","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T11:12:49Z","timestamp":1721128369000},"page":"2593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Multi-Level Adaptive Lightweight Net for Damaged Road Marking Detection Based on Knowledge Distillation"],"prefix":"10.3390","volume":"16","author":[{"given":"Junwei","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Beijing International Data Exchange, Beijing 100027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7901-1252","authenticated-orcid":false,"given":"Xiangqiang","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100091, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0821-6549","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Xiang","family":"Ren","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1377-8394","authenticated-orcid":false,"given":"Dongliang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9360-2494","authenticated-orcid":false,"given":"Wenqiu","family":"Qu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaohan","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Peifen","family":"Pan","sequence":"additional","affiliation":[{"name":"China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Morrissett, A. 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