{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:59:49Z","timestamp":1776891589636,"version":"3.51.2"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Program-General Projects-Surface Projects of Shaanxi Provincial Department of Science and Technology","award":["2025JC-YBMS-746"],"award-info":[{"award-number":["2025JC-YBMS-746"]}]},{"name":"Scientific Research Plan Project of the Education Department of Shaanxi Province-Youth Innovation Team Project","award":["23JP071"],"award-info":[{"award-number":["23JP071"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Detecting low-altitude, slow-speed, small (LSS) UAVs is especially challenging in low-visibility scenes (low light, haze, motion blur), where inherent uncertainties in sensor data and object appearance dominate. We propose GAME-YOLO, a novel detector that integrates a Bayesian-inspired probabilistic reasoning framework with Global Attention and Multi-Scale Enhancement to improve small-object perception and sub-pixel-level localization. Built on YOLOv11, our framework comprises: (i) a visibility restoration front-end that probabilistically infers and enhances latent image clarity; (ii) a global-attention-augmented backbone that performs context-aware feature selection; (iii) an adaptive multi-scale fusion neck that dynamically weights feature contributions; (iv) a sub-pixel-aware small-object detection head (SOH) that leverages high-resolution feature grids to model sub-pixel offsets; and (v) a novel Shape-Aware IoU loss combined with focal loss. Extensive experiments on the LSS2025-DET dataset demonstrate that GAME-YOLO achieves state-of-the-art performance, with an AP@50 of 52.0% and AP@[0.50:0.95] of 32.0%, significantly outperforming strong baselines such as LEAF-YOLO (48.3% AP@50) and YOLOv11 (36.2% AP@50). The model maintains high efficiency, operating at 48 FPS with only 7.6 M parameters and 19.6 GFLOPs. Ablation studies confirm the complementary gains from our probabilistic design choices, including a +10.5 pp improvement in AP@50 over the baseline. Cross-dataset evaluation on VisDrone-DET2021 further validates its generalization capability, achieving 39.2% AP@50. These results indicate that GAME-YOLO offers a practical and reliable solution for vision-based UAV surveillance by effectively marrying the efficiency of deterministic detectors with the robustness principles of Bayesian inference.<\/jats:p>","DOI":"10.3390\/e27121263","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T13:45:07Z","timestamp":1766065507000},"page":"1263","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GAME-YOLO: Global Attention and Multi-Scale Enhancement for Low-Visibility UAV Detection with Sub-Pixel Localization"],"prefix":"10.3390","volume":"27","author":[{"given":"Ruohai","family":"Di","sequence":"first","affiliation":[{"name":"School of Cross-Innovation, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Hao","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Yuanzheng","family":"Ma","sequence":"additional","affiliation":[{"name":"Test Center, National University of Defense Technology, Xi\u2019an 710100, China"}]},{"given":"Jinqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Ruoyu","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"ref_1","first-page":"319","article-title":"Aams-yolo: A small object detection method for UAV capture scenes based on YOLOv7","volume":"28","author":"Liu","year":"2025","journal-title":"Clust. 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