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This paper proposes a crack segmentation method based on the DeepLabV3+ model, which integrates the attention mechanism and dynamic snake convolution. The attention module enhances both channel and spatial attention. The dynamic snake convolution is employed to precisely capture features of linear structures, with adaptability to focus on elongated and tortuous local structures. The module\u2019s design ensures effective preservation of crucial information across different global morphologies, and significantly improves the extraction of detailed features, leading to more accurate segmentation outcomes. The proposed method demonstrates state-of-the-art results on two benchmark datasets, DeepCrack and Crack500. On DeepCrack, it achieves an accuracy, recall,\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:msub>\n                          <mml:mi>F<\/mml:mi>\n                          <mml:mn>1<\/mml:mn>\n                        <\/mml:msub>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    , and MIoU scores of 94.1%, 93.6%, 89.4%, and 89.4%, respectively. On Crack500, the recall,\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\">\n                        <mml:msub>\n                          <mml:mi>F<\/mml:mi>\n                          <mml:mn>1<\/mml:mn>\n                        <\/mml:msub>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    , and MIoU scores are 89.4%, 83.9%, and 82.9%, respectively.\n                  <\/jats:p>","DOI":"10.1177\/18758967251356858","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T07:58:40Z","timestamp":1752134320000},"page":"805-821","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deeplab-based Road Crack Segmentation with Attention and Dynamic Convolution"],"prefix":"10.1177","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6113-4992","authenticated-orcid":false,"given":"Xiao","family":"Ye","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7767-5987","authenticated-orcid":false,"given":"Kaiqiong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4771-3559","authenticated-orcid":false,"given":"Jing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9307-1974","authenticated-orcid":false,"given":"Kang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106142"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_3_5_1","doi-asserted-by":"crossref","unstructured":"Chen L. 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