SRPCNet: Self-Reinforcing Perception Coordination Network for Seamless Steel Pipes Internal Surface Defect Detection

Abstract

Seamless steel pipes (SSPs) are vital material for industries. However, internal surface defects (ISDs) in SSPs are challenging to detect, and will significantly affect SSPs performance and lifespan. Existing detection methods are labor-intensive and have low visualization of detection results. Therefore, this article present a novel detection system comprising the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u>ipeline <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u>ll-aspect internal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>urface defect <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u>piral detecting robot and an interactive visualization software. After testing in the SSPs factory, the system achieves comprehensive, wireless and efficient detection and visualization for ISDs. In addition, we construct a dataset for ISDs in SSPs, named as SSP2000. The dataset contains 2000 images across nine defect categories, with many challenges in it. Furthermore, to accurately detect defects, we design the SRPCNet which can effectively address the challenges. Specifically, we first use the synergize perception augmentation module to enrich the feature space and to enhance the perception. Then, the hierarchical attention integrate module merges deep and shallow features using adaptive attention weights. Finally, the bilateral self-fusion module fully exploits intralayer features and produce prediction results. The proposed SRPCNet outperforms existing methods on eight evaluation metrics.