{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:24:06Z","timestamp":1760149446883,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T00:00:00Z","timestamp":1691193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901015","2022ZHCG0060"],"award-info":[{"award-number":["61901015","2022ZHCG0060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Province Science and Technology Achievement Transformation Demonstration Project","award":["61901015","2022ZHCG0060"],"award-info":[{"award-number":["61901015","2022ZHCG0060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection performance on multi-scale objects, and limited dataset availability. To address these issues, this paper proposes a novel airport detection network (TPH-YOLOv5-Air) based on adaptive spatial feature fusion (ASFF). Firstly, we construct an Airport Confusing Object Dataset (ACD) specifically tailored for remote sensing scenarios containing 9501 instances of airport confusion objects. Secondly, building upon the foundation of TPH-YOLOv5++, we adopt the ASFF structure, which not only enhances the feature extraction efficiency but also enriches feature representation. Moreover, an adaptive spatial feature fusion (ASFF) strategy based on adaptive parameter adjustment module (APAM) is proposed, which improves the feature scale invariance and enhances the detection of airports. Finally, experimental results based on the ACD dataset demonstrate that TPH-YOLOv5-Air achieves a mean average precision (mAP) of 49.4%, outperforming TPH-YOLOv5++ by 2% and the original YOLOv5 network by 3.6%. This study contributes to the advancement of airport detection in remote sensing scenes and demonstrates the practical application potential of TPH-YOLOv5-Air in this domain. Visualization and analysis further validate the effectiveness and interpretability of TPH-YOLOv5-Air. The ACD dataset is publicly available.<\/jats:p>","DOI":"10.3390\/rs15153883","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T10:25:36Z","timestamp":1691231136000},"page":"3883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["TPH-YOLOv5-Air: Airport Confusing Object Detection via Adaptively Spatial Feature Fusion"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2154-228X","authenticated-orcid":false,"given":"Qiang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrics and Information Engineering, Beihang University, Beijing 100191, China"},{"name":"UAV Industry Academy, Chengdu Aeronautic Polytechnic, Chengdu 610100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5180-7889","authenticated-orcid":false,"given":"Wenquan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Electrics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8734-9879","authenticated-orcid":false,"given":"Lifan","family":"Yao","sequence":"additional","affiliation":[{"name":"Qingdao Research Institute of Beihang University, Qingdao 266000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5707-6805","authenticated-orcid":false,"given":"Chen","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Department of Electrics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6590-0016","authenticated-orcid":false,"given":"Binghao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electrics and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7732-3527","authenticated-orcid":false,"given":"Lijiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrics and Information Engineering, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., and Gao, Y. 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