DRE-YOLO

Detection of Unexploded Ordnance (UXO) based on drone platforms is crucial for public safety; however, it faces three significant challenges in complex outdoor environments: abrupt variations in target scale, degradation of edge information due to vegetation occlusion, and a high false detection rate caused by the similarity in texture between targets and backgrounds.Existing methods for detecting rotating targets exhibit inherent limitations: the traditional fixed receptive field design results in insufficient discriminability of small target features; in scenarios with vegetation occlusion, upsampling operations struggle to mitigate the degradation of geometric information at target edges; and significant background noise substantially reduces detection reliability.To address these challenges, this paper introduces DRE-YOLO, a multi-scale dynamic refinement network framework. First, a Deformable Adaptive Module (DAM-C3) is designed to dynamically adjust the receptive field size through deformable convolutional kernels while integrating local-global contexts, significantly enhancing scale adaptability. Secondly, an Efficient Shift-Upsampling (ES-Upsample) is developed, which combines a decoupled upsampling mechanism with a channel displacement fusion layer to effectively suppress edge degradation caused by vegetation occlusion. Finally, a Dual-Polarity Attention (DPA) is introduced in the detection head, utilizing a dynamic feature scaler and differential polarity modeling to substantially mitigate interference from background textures that are similar to the targets.