Abstract: Aming at addressing the issue of low detection accuracy in detecting small road targets in drone aerial images, this work proposes a target detection model based on the fusion of SKNet convolutional kernel attention mechanism and YOLOv5s, which has enhanced ability to extract and recognize feature information of small ground targets. Based on this improvement, a Python deep learning development environment configured with Visual Studio Code was used to conduct performance testing experiments on SKNet+YOLOv5s. The experimental results show that when using VisDrone2019 as the dataset for training, compared to several conventional attention mechanisms such as SENet+YOLOv5s and CBAM+YOLOv5s, the detection accuracy of SKNet+YOLOv5s is higher.