In view of the frequent leakage of oil pipelines in key areas such as power plant oil depots and chemical water workshops, a pipeline leak detection method in key areas of power plants based on improved YOLO v5 is proposed. The improved YOLO v5 detection algorithm first incorporates CBAM module to strengthen the learning of regional features of pipeline oil leakage images. The CBAM makes the model more focused on the extraction of pipeline leakage features, and weakens the influence of complex backgrounds on detection results. Secondly, a bidirectional feature pyramid network is used for multi-scale feature fusion. It also reduces redundant calculation, and improves the detection ability of the algorithm for small targets. Finally, Focal EIoU Loss is used as the loss function to make the regression process more focused on high-quality anchor boxes. It improves the regression accuracy, speeds up the convergence speed, and improve the robustness of the model. The experimental results show that the improved algorithm performs well in real samples, with an average accuracy rate of 79. 6%, which is 38. 4% higher than the original YOLO v5s algorithm. The false positive rate and the false negative rate in the complex background of the power plant are significantly reduced. It shows that the improved YOLO v5 detection algorithm can be effectively applied in the actual production environment.