High-precision target detection in complex UAV scenarios: a multi-scale enhancement framework

Abstract

Unmanned aerial vehicle (UAV) imagery is widely used in urban monitoring, public security, and disaster assessment. However, object detection in UAV scenes faces multiple challenges, including a high proportion of small objects, severe occlusion in crowded areas, complex background textures, and image degradations such as haze, which often cause generic detectors to suffer from missed detections, false alarms, and unstable localization. To address these issues, we propose a lightweight multi-scale enhanced detection framework tailored for complex UAV scenarios. Built upon a MobileNet backbone, the proposed framework introduces a multi-scale enhancement module that constructs a feature pyramid and incorporates a scale-adaptive fusion mechanism to dynamically reweight the contributions of features from different scales. In addition, a fine-grained detail enhancement branch is deployed at high-resolution levels to strengthen edge and texture cues, while a context compensation module is designed to alleviate local uncertainty under dense occlusion and low-contrast conditions, thereby improving small-object separability and localization stability. Experimental results demonstrate that our method achieves strong performance on both VisDrone-DET and HazyDet, reaching mAP@0.5 = 0.312 and mAP@0.5:0.9 = 0.167 on VisDrone-DET, and mAP@0.5 = 0.719 and mAP@0.5:0.9 = 0.483 on HazyDet. The proposed method also shows favorable efficiency on an RTX 4060 Ti desktop GPU, indicating its real-time inference potential under the reported desktop hardware setting.