低空视角下改进无人机小目标检测算法OA

In the context of the rapidly growing low-altitude economyand the urgent need for high-precision,lightweight UAV object detection in agriculture,logistics,and emergency rescue,this paper tackles the challenges of low target pixel occupancy,environmental occlusion,and severe perspective distortion in low-altitude imagery.Based on the RT-DETR algorithm,an enhanced detection model—Cross-scale Alignment and Position Encoding Enhanced RT-DETR(CAPE-RT-DETR)—is proposed.Firstly,to overcome the limitation of traditional static convolution kernels in terms of feature extraction flexibility under complex backgrounds,this paper proposes a feature enhancement moduleintegrating dynamic convolution kernel generation and gated feature selection,termed C2ML.By utilizing a Large Kernel Predictor(LKP)to dynamically generate spatially adaptive convolution kernels,and combining it with a gated feature selection mechanism to eliminate redundant background information,the module significantly enhances the model's ability to extract and filter critical features.Secondly,to address the geometric distortion and spatially non-uniform deformation characteristic of aerial perspectives,a learnable positional encoding is integrated with the multi-head self-attention mechanism to construct an enhanced position-aware interaction module,termed AIFP.By learning spatial prior information in an end-to-end manner,this module effectively improves the model's perceptual sensitivity and localization accuracy with respect to the low-altitude-specific spatial structures.Finally,to resolve the pixel misalignment problem caused by simple upsampling in multi-scale feature fusion,a a cross-scale feature calibration(CSFC)module is introduced.This module utilizes a pyramid scene parsing structure to integrate sparse global context and employs a dual-path convolution and grid sampling mechanism to explicitly compensate for cross-scale alignment biases,thereby achieving consistent representation of semantic information.Experimental results on the ALU and VisDrone2019 datasets demonstrate that CAPE-RT-DETR outperforms the baseline algorithm in terms of parameter count,accuracy,and model size.Meanwhile,ablation experiments validate the effectiveness and synergy of the three improved modules.This research provides a high-precision and lightweight methodological foundation and theoretical support for real-time UAV object detection in complex scenarios.