To deal with the challenges faced by target detection algorithms due to the small scale, weak features, and high background interference in images of a drone dataset, a multi-scale target detection algorithm for unmanned aerial vehicle images in complex scenarios is proposed. This algorithm enhances the overall accuracy, reduces false negatives and positives, through the incorporation of modules such as DConv, AIFI, and Dyhead. These components address the limitations of the original network in handling multi-scale targets. Furthermore, the use of the DIoU loss function improves the model′s convergence capability. The effectiveness of this approach is demonstrated through its application in detecting multi-scale targets on the VisDrone-DET2019 dataset. Compared to the original network, there is a 3.7% increase in precision, a 1.2% increase in recall rate, and a 2.3% improvement in average accuracy. Moreover, extensive experiments demonstrated that the proposed algorithm exhibits strong robustness and excellent overall performance, suggesting significant industrial application potential.