YOLO-PD: Abnormal Signal Detection in Gas Pipelines Based on Improved YOLOv7

Natural gas pipelines are an important mode of transportation. How to quickly and accurately detect abnormal signals that occur during pipeline operation is crucial to judging the pipeline status. In this article, an abnormal signal detection method based on improved YOLOv7 (YOLO-PD) was proposed to address the low detection accuracy of abnormal signals and the dependence on manual signal segmentation scale setting in pipeline operation. First, a new refined channel attention (RCA) module was proposed to enhance network feature extraction. Second, a MaxPooling (MP) with RCA module was designed to strengthen network feature extraction and fusion capabilities. Furthermore, the atrous spatial pyramid pooling (ASPP) with RCA module was used to expand the model's receptive field and enhance the core information features, thereby improving the detection accuracy of the model for multiscale objects. Finally, the SIoU loss function was utilized to expedite network convergence and optimize the model training process. The experimental results demonstrate that YOLO-PD exhibits high accuracy and speed, with an mAP at 0.5 of 0.996 and a frame rate of 65. In comparison to other prevalent target detection algorithms, the method proposed in this article can effectively identify abnormal signals in pipelines and achieve higher detection accuracy.