Deep Learning Based Steel Pipe Weld Defect Detection

TL;DR

This paper applies YOLOv5, a deep learning object detection algorithm, to steel pipe weld defect detection, demonstrating improved accuracy and real-time multi-classification capabilities over traditional methods.

Contribution

It introduces YOLOv5 for steel pipe weld defect detection, outperforming Faster R-CNN in accuracy and multi-classification, advancing industrial automation inspection methods.

Findings

YOLOv5 achieves higher detection accuracy.

Supports multi-class defect classification.

Meets real-time detection criteria.

Abstract

Steel pipes are widely used in high-risk and high-pressure scenarios such as oil, chemical, natural gas, shale gas, etc. If there is some defect in steel pipes, it will lead to serious adverse consequences. Applying object detection in the field of deep learning to pipe weld defect detection and identification can effectively improve inspection efficiency and promote the development of industrial automation. Most predecessors used traditional computer vision methods applied to detect defects of steel pipe weld seams. However, traditional computer vision methods rely on prior knowledge and can only detect defects with a single feature, so it is difficult to complete the task of multi-defect classification, while deep learning is end-to-end. In this paper, the state-of-the-art single-stage object detection algorithm YOLOv5 is proposed to be applied to the field of steel pipe weld defect detection, and compared with the two-stage representative object detection algorithm Faster R-CNN. The experimental results show that applying YOLOv5 to steel pipe weld defect detection can greatly improve the accuracy, complete the multi-classification task, and meet the criteria of real-time detection.