{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T23:35:31Z","timestamp":1791588931354,"version":"4.3.4"},"reference-count":26,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate detection of oil leakage spots is essential for oilfield safety and environmental protection. However, UAV-based inspection in onshore oilfields often suffers from complex illumination conditions, such as low light, backlighting, and mixed shadows, which simultaneously degrade image visibility and obscure leakage-sensitive features, thereby causing missed detection of minute and weak-texture oil leakage targets. Unlike generic low-light enhancement or object detection tasks, the core challenge of onshore UAV oil leakage inspection lies in preserving leakage-oriented fine cues during enhancement while improving the detector\u2019s ability to distinguish leakage targets from highly confusing oilfield backgrounds. To address this task-specific challenge, we propose a collaborative low-light enhancement and detection framework that jointly optimizes leakage-detail-preserving enhancement and multi-scale interference-suppressed detection. Specifically, an improved Retinex-based enhancement network is designed by integrating multi-scale feature aggregation, NAFNet-based denoising, and a CBAM attention mechanism to enhance brightness while preserving leakage details. The enhanced images are then fed into an improved YOLOv11 detector, where an AC-FPN module is adopted to strengthen multi-scale feature fusion and suppress background interference. Experiments on UAV oilfield datasets demonstrate that the proposed method achieves a precision of 94.25% and a mean average precision (mAP) of 87.54%, outperforming existing approaches. The proposed framework provides an effective and robust solution for oil leakage spot detection under complex illumination.<\/jats:p>","DOI":"10.3390\/s26061819","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T12:59:44Z","timestamp":1773406784000},"page":"1819","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["UAV-Based Oil Leakage Spot Detection Under Complex Illumination via a Collaborative Low-Light Enhancement and Detection Framework"],"prefix":"10.3390","volume":"26","author":[{"given":"Yunsheng","family":"Ha","sequence":"first","affiliation":[{"name":"College of Management and Economics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4222-1285","authenticated-orcid":false,"given":"Huili","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"ref_1","first-page":"28","article-title":"Oil Spill Detection Algorithm for Oilfield Operations Sites","volume":"18","author":"Jin","year":"2022","journal-title":"Syst. 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