Multi-architecture optimization of pipeline inner wall defect detection algorithm based on YOLOv8

Abstract

As traditional computer vision technology struggles to meet the demands for accurate detection of modern supply pipes. A detection model based on an improved YOLOv8 network has been proposed. First, the Inverted Residual Mobile Block (iRMB) is integrated into the backbone network. This effectively enhances the feature representation capability for extracting image defects. Next, a shift-wise operator is introduced to simulate the effects of using a large convolutional kernel at a lower computational cost while improving performance. Finally, GSConv replaces the Conv layer in the neck network, and the VoVGSCSP module substitutes the C2f module in the neck network to further enhance the algorithm’s detection accuracy for corroded regions. Experimental results demonstrate that the mean Average Precision (mAP) of the improved algorithm reaches 95.0 % on the dataset of pipeline inner wall corrosion defects. This provides an accurate method for the intelligent identification of corrosion defects on the pipeline dataset.