Research on Real-time Detection of Pipeline Weld Defects Based on Lightweight Neural Networks

Abstract

In the field of pipeline weld defect detection, common object detection algorithms have high complexity and huge computational load, making it difficult to meet the real-time monitoring requirements of pipeline weld defects on pipeline production lines. To address this issue, this paper proposes a lightweight pipeline weld defect detection model YOLOv8-BVS based on the YOLOv8 object detection framework. The model introduces the BRA module to improve the recognition ability of small defects. To further improve the accuracy of model recognition, a lightweight upsampling algorithm CARAFE is used in the feature fusion network to improve the quality and richness of fused features. Finally, the experimental results showed that the model parameters were 1.56M, which was only 51.6% of the baseline, while the average accuracy reached 87.9%, an improvement of 3.4% compared to the baseline. This verified that the YOLOv8 BVS model met the requirements of online detection of pipeline weld defects while ensuring detection quality.