Aiming at the problems of complex background of aerial images， dense targets， and uneven target scale distribution in UAV traffic inspection， a multi-feature crossover under cross-layer attentional interaction （Multi-feature crossover under cross-layer attentional interaction，MCAI） UAV target detection algorithm was proposed. Firstly， an Adaptive Cross-layer Attentional Interaction （Adaptive Cross-layer Attentional Interaction，ACAI） module was designed in the backbone network part so that the model focused on the key feature regions to achieve effective screening of global key feature information， thus fading the influence of the complex background. Secondly， a deformable self-attentive encoder （Deformable Encoder， DeEncoder） was designed， which compensated for the lost target features by expanding the feature layer receptive field. Finally， in order to effectively identify tiny targets at different scales in the region， the multi-scale cross-fusion module （Multi-scale cross fusion module，MSCF） was proposed， which fused shallow spatial information and deep semantic information by combining the wavelet transform and feature representation in order to efficiently capture the fine-grained features of targets at different scales. The experimental results on the VisDrone 2019-DET， BDD-100K dataset， and LZTraffic Video dataset show that MCAI improves mAP0.5 by 3%， 2.2%， and 4.5%， respectively， compared to the RT-DETR model， which significantly improves the detection accuracy of the UAV inspection. In addition， in the cloudy and rainy scenario， the mAP0.5 of MCAI improves by 2.1% compared to the RT-DETR model， with better extreme weather robustness performance.