Dense metal corrosion depth estimation - Li, Yanping; Li, Honggang; Guan, Yong; Zhang, Xinyu; Zhao, Xiaomei (Frontiers in Physics, 2023)
Introduction: Metal corrosion detection is important for protecting lives and property. X-ray inspection systems are widely used because of their good penetrability and visual presentation capability. They can visually display both external and internal corrosion defects. However, existing X-ray-based defect detection methods cannot present and estimate the dense corrosion depths. To solve this problem, we propose a dense metal corrosion depth estimation method based on image segmentation and inpainting. Methods: The proposed method employs an image segmentation module to segment metal corrosion defects and an image inpainting module to remove these segmented defects. It then calculates the pixel-level dense corrosion depths using the X-ray images before and after inpainting. Moreover, to address the difficulty of acquiring training images with ground-truth dense corrosion depth annotations, we propose a virtual data generation method for creating virtual corroded metal X-ray images and their corresponding ground-truth annotations. Results: Experiments on both virtual and real datasets show that the proposed method successfully achieves accurate dense metal corrosion depth estimation. Discussion: In conclusion, the proposed virtual data generation method can provide effective and sufficient training samples, and the proposed dense metal corrosion depth estimation framework can produce accurate dense corrosion depths.