As manual identification of ground-penetrating radar (GPR) pipeline images faces the problems of low efficiency, large errors, and high costs, this study proposes an intelligent pipeline target detection method based on improved Cascade R-CNN. First, the GPR pipeline image data set is preprocessed to improve data quality. ResNeXt is used instead of ResNet as the backbone network to extract target feature information, and a multi-scale feature fusion module FPN is added to fuse high-level features to low-level features to enhance the expressiveness of low-level features. Secondly, the Gaussian non-maximum suppression (NMS) method Soft-NMS is used to obtain more accurate candidate boxes, and Smooth_L1 is taken as the loss function, which accelerates model convergence and reduces the probability of gradient explosion during training. Finally, for the special shape features of the pipeline target, the appropriate aspect ratio and size of the anchor boxes are set to improve the quality of generated anchor boxes. The experimental results demonstrate that the proposed method achieves the intelligent detection of underground pipeline targets with complex features, and the average accuracy of target detection reaches 94.7%, which is 10.1% higher than that of the Cascade R-CNN method.