Aiming at the problem of waste oil leakage from power plant rainwater outlets in monitoring images, a pollutant leakage detection algorithm based on improved Faster R-CNN is proposed. The improved Faster R-CNN detection algorithm first uses ResNet-50 as the backbone network, and builds a multi-scale feature map pyramid structure ( FPN) on this basis to achieve information fusion between high-level semantics and low-level semantics, and improve detection accuracy; Secondly, the CIoU loss and DIoU-NMS methods are used to improve the accuracy of bounding box regression in Faster R-CNN; Finally, by introducing Focal Loss function, it solves the problem of unbalanced positive and negative samples in the R-CNN training stage caused by redundant anchor generated by RPN network. The experimental results show that the improved algorithm performs well in real samples, and the accuracy rate reaches 90. 2%. Compared with the original Faster R-CNN algorithm, the accuracy rate is improved, and the false positive rate and false negative rate are significantly reduced. It can be effectively used in the actual environment.