{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:31:10Z","timestamp":1768509070709,"version":"3.49.0"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2024YFC3014400"],"award-info":[{"award-number":["2024YFC3014400"]}]},{"name":"Civil Aviation Administration of China (CAAC) Safety Capability Project","award":["ASSA2024\/95"],"award-info":[{"award-number":["ASSA2024\/95"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>To address the challenges in detecting surface damage on general aviation aircraft skin\u2014such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy\u2014this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is integrated into the shallow network to enhance the capture of fine damage details. Second, an HMFHead detection head is introduced to mitigate scale variation effects through progressive semantic fusion and edge-aware constraints. Third, an LDown downsampling module is designed to construct a multi-scale feature fusion architecture. This module reduces redundancy via cross-level interaction and a lightweight kernel design, thereby decreasing the number of parameters and computational cost. Additionally, a DySample-based dynamic sampling operator is proposed to preserve local details through proximity-aware sampling while enriching the contextual semantics of distant damage features, effectively improving recognition performance. Experiments on a self-constructed general aviation aircraft skin damage dataset show that GAD-YOLO achieves 87.4% precision, 80.4% recall, 86.6% mAP@0.5, and 59.7% mAP@0.5:0.95. These results outperform the YOLOv11n baseline by 2.0%, 9.4%, 6.7%, and 7.6%, respectively. The proposed method significantly improves detection performance and provides a valuable reference for intelligent inspection and maintenance in general aviation.<\/jats:p>","DOI":"10.3390\/a19010061","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:13:01Z","timestamp":1768209181000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GAD-YOLO: A Sight-Distance Adaptive Detection Algorithm for General Aviation Aircraft Skin Damage"],"prefix":"10.3390","volume":"19","author":[{"given":"Tao","family":"Wu","sequence":"first","affiliation":[{"name":"China Academy of Civil Aviation Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jifei","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanhai","family":"Wang","sequence":"additional","affiliation":[{"name":"China Academy of Civil Aviation Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"China Academy of Civil Aviation Science and Technology, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghong","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1007\/s11666-014-0130-1","article-title":"Aircraft skin restoration and evaluation","volume":"23","author":"Yandouzi","year":"2014","journal-title":"J. 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