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Unmanned Aerial Vehicles (UAVs), with their high mobility and wide coverage, have become a vital tool for this monitoring task. However, this application is hampered by the dual challenges of robust multi-scale object detection and lightweight model deployment on computationally limited platforms. To address the trade-off between accuracy and efficiency, this study proposes an efficient deep learning-based detection method, termed Scale-Oriented Detection Network (SODNet). Specifically, we propose an Efficient Context Feature Pyramid Network (ECFPN) to enhance multi-scale feature representation. Additionally, a shared decoupled head with a Multi-Scale Grouped Fusion (MSGF) module strengthens feature extraction while reducing computational costs. Furthermore, a channel pruning strategy is employed to compress the model, notably improving inference speed. Experimental results demonstrate that SODNet achieves an AP@50 of 89.9% and a precision of 91.1%, representing improvements of 1.2% and 2.7% over the baseline model, respectively. Meanwhile, parameters and GFLOPs are reduced by 77.5% and 73.6%. On a deployed edge device, SODNet achieves 40.3 FPS. These findings indicate that SODNet gains substantial computational efficiency while maintaining excellent detection performance, making it ideal for resource-constrained UAV scenarios and offering a feasible solution for intelligent environmental supervision.<\/jats:p>","DOI":"10.1038\/s41598-026-45595-x","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:25:10Z","timestamp":1774531510000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SODNet: a scale-oriented detection network for efficient UAV-based sewage outfall detection"],"prefix":"10.1038","volume":"16","author":[{"given":"Luping","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozhou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangming","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiguo","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"45595_CR1","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1038\/s41893-021-00718-2","volume":"4","author":"J Woodward","year":"2021","unstructured":"Woodward, J., Li, J., Rothwell, J. & Hurley, R. 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