To address the challenges of extreme scale variations，dense occlusion of small targets，and complex background interference in unmanned aerial vehicle （UAV）-based target detection，this paper proposes a cross-layer dynamic detection network based on an improved YOLOv10 for the detection of small target via UAV aerial photography.A dual-branch cross-layer feature fusion pyramid network for replacing the original pyramid network is designed to resolve the problem of insufficient detail preservation for small targets in traditional methods.A channel-shuffling depth-wise upsampling module is developed，which combines channel shuffle operations with depth-wise separable convolutions and enhances the edge features of small targets through high-frequency residual connections.An end-to-end dynamic detection head is adopted to replace the original detection head，and a dynamic weighting mechanism is introduced，which enables the adaptive adjustment of feature representations at each position based on contextual information.Experimental results show that the proposed detection network achieves mAP0.5 of 53.3% and mAP0.5：0.95 of 33.2% on the VisDrone 2019 validation set，which are improveed by 12.7% and 9% ，respectively，compared to YOLOv10s，while reduces the model parameters by 23.7% and achieves an FPS of 79.The proposed algorithm significantly enhances the detection accuracy while maintaining excellent inference speed.