{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:39:08Z","timestamp":1781973548032,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Programs","award":["2022YFC3004405"],"award-info":[{"award-number":["2022YFC3004405"]}]},{"name":"National Key Research and Development Programs","award":["42061073"],"award-info":[{"award-number":["42061073"]}]},{"name":"National Key Research and Development Programs","award":["[2020]1Z056"],"award-info":[{"award-number":["[2020]1Z056"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFC3004405"],"award-info":[{"award-number":["2022YFC3004405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42061073"],"award-info":[{"award-number":["42061073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["[2020]1Z056"],"award-info":[{"award-number":["[2020]1Z056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005329","name":"Natural Science Foundation of Guizhou Province","doi-asserted-by":"publisher","award":["2022YFC3004405"],"award-info":[{"award-number":["2022YFC3004405"]}],"id":[{"id":"10.13039\/501100005329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005329","name":"Natural Science Foundation of Guizhou Province","doi-asserted-by":"publisher","award":["42061073"],"award-info":[{"award-number":["42061073"]}],"id":[{"id":"10.13039\/501100005329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005329","name":"Natural Science Foundation of Guizhou Province","doi-asserted-by":"publisher","award":["[2020]1Z056"],"award-info":[{"award-number":["[2020]1Z056"]}],"id":[{"id":"10.13039\/501100005329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Quickly and accurately assessing the damage level of buildings is a challenging task for post-disaster emergency response. Most of the existing research mainly adopts semantic segmentation and object detection methods, which have yielded good results. However, for high-resolution Unmanned Aerial Vehicle (UAV) imagery, these methods may result in the problem of various damage categories within a building and fail to accurately extract building edges, thus hindering post-disaster rescue and fine-grained assessment. To address this issue, we proposed an improved instance segmentation model that enhances classification accuracy by incorporating a Mixed Local Channel Attention (MLCA) mechanism in the backbone and improving small object segmentation accuracy by refining the Neck part. The method was tested on the Yangbi earthquake UVA images. The experimental results indicated that the modified model outperformed the original model by 1.07% and 1.11% in the two mean Average Precision (mAP) evaluation metrics,\u00a0mAPbbox50 and mAPseg50, respectively. Importantly, the classification accuracy of the intact category was improved by 2.73% and 2.73%, respectively, while the collapse category saw an improvement of 2.58% and 2.14%. In addition, the proposed method was also compared with state-of-the-art instance segmentation models, e.g., Mask-R-CNN and YOLO V9-Seg. The results demonstrated that the proposed model exhibits advantages in both accuracy and efficiency. Specifically, the efficiency of the proposed model is three times faster than other models with similar accuracy. The proposed method can provide a valuable solution for fine-grained building damage evaluation.<\/jats:p>","DOI":"10.3390\/s24134371","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T12:30:59Z","timestamp":1720182659000},"page":"4371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An Improved Instance Segmentation Method for Fast Assessment of Damaged Buildings Based on Post-Earthquake UAV Images"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5572-5415","authenticated-orcid":false,"given":"Ran","family":"Zou","sequence":"first","affiliation":[{"name":"School of Information Science, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science, Shanghai Ocean University, Shanghai 201306, China"},{"name":"National Earthquake Response Support Service, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5565-3022","authenticated-orcid":false,"given":"Haiyan","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information Science, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delong","family":"Tang","sequence":"additional","affiliation":[{"name":"Guizhou Provincial Seismological Bureau, Guiyang 550001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Science, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ta\u015fkin, G., Erten, E., and Alata\u015f, E.O. 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