<p>Object detection in unmanned aerial vehicle (UAV) images has become an important research area in computer vision due to its unique value and challenges. UAV images are characterized by densely distributed small targets, significant changes in target scale, and background noise, which affect the accuracy and reliability of detection. To address these issues, we propose an small target detection network based on Enhanced Scale Sequence Fusion and channel space fusion cross-attention mechanism, called CSFCANet.To tackle the high proportion of small targets and scale variation in UAV images, we employ Enhanced Scale Sequence Fusion, integrating fine-grained information from shallow feature maps and semantic information from deep feature maps. Additionally, we incorporate an tiny target detection head to enhance the network’s ability to extract fine-grained information features for small targets. To address the issue of background noise, we propose a channel space fusion cross-attention mechanism, which first performs attention calculation on local patch block feature maps, and then performs attention calculation global patch blocks. This captures both long-range dependencies and detailed information. The method for calculating attention combines spatial description information and channel description information.Extensive experiments were conducted to validate the effectiveness of the model on the VisDrone benchmark dataset, UAVDT dataset and our self-made UAV power inspection dataset PIDrone. In comparison to the YOLOv8s model, the CSFCANet demonstrated an improvement in mAP of 7% on the PIDrone, 2.4% on the VisDrone, and 3.6% on the UAVDT.</p>