{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:06:40Z","timestamp":1772813200541,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686547","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:p>Railway infrastructure inspection is crucial for ensuring operational safety and improving efficiency. However, traditional manual inspection methods are not only time-consuming and labor-intensive but also prone to human errors, leading to fluctuations in inspection quality and efficiency. To address this issue, this paper proposes an unmanned aerial vehicle (UAV) automated inspection method based on a Unified Training Fusion Reinforcement Learning Network (UTFN), which combines Unified Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) algorithms for autonomous route planning and navigation. This approach provides precise support for task path planning in complex geographical environments, overcoming the limitations of traditional methods in such scenarios. The integration of these technologies results in a highly intelligent and automated UAV operation and control inspection system, minimizing human intervention while improving inspection accuracy and efficiency. Experimental results demonstrate that the proposed system effectively reduces operational costs, and enhances operational control efficiency, ensuring the smooth completion of tasks.<\/jats:p>","DOI":"10.3233\/faia260014","type":"book-chapter","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:42Z","timestamp":1772792442000},"source":"Crossref","is-referenced-by-count":0,"title":["A Unified Reinforcement Learning Framework for Comprehensive UAV Railway Inspection"],"prefix":"10.3233","author":[{"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"China Academy of Railway Sciences Corporation Limited, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmin","family":"Gao","sequence":"additional","affiliation":[{"name":"China Academy of Railway Sciences Corporation Limited, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zexi","family":"Zheng","sequence":"additional","affiliation":[{"name":"China Academy of Railway Sciences Corporation Limited, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowei","family":"Liu","sequence":"additional","affiliation":[{"name":"China Academy of Railway Sciences Corporation Limited, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zibo","family":"Ouyang","sequence":"additional","affiliation":[{"name":"China Academy of Railway Sciences Corporation Limited, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:42Z","timestamp":1772792442000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"ISBN":["9781643686547"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260014","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}