Abstract
Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints, and in welding defect inspection, among nondestructive inspections, radiographic inspection is widely applied during the production process. To perform nondestructive inspection, the completed weldment must be transported to the nondestructive inspection station, which is expensive; consequently, automation of welding defect detection is required. Recently, at several processing sites of companies, continuous attempts are being made to combine deep learning to detect defects more accurately. Preprocessing for welding defects in radiographic inspection images should be prioritized to automatically detect welding defects using deep learning during radiographic nondestructive inspection. In this study, by analyzing the pixel values, we developed an image preprocessing method that can integrate the defect features. After maximizing the contrast between the defect and background in radiographic through CLAHE (contrast-limited adaptive histogram equalization), denoising (noise removal), thresholding (threshold processing), and concatenation were sequentially performed. The improvement in detection performance due to preprocessing was verified by comparing the results of the application of the algorithm on raw images, typical preprocessed images, and preprocessed images. The mAP for the training data and test data was 84.9% and 51.2% for the preprocessed image learning model, whereas 82.0% and 43.5% for the typical preprocessed image learning model and 78.0%, 40.8% for the raw image learning model. Object detection algorithm technology is developed every year, and the mAP is improving by approximately 3% to 10%. This study achieved a comparable performance improvement by only preprocessing with data.
Highlights
Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints in order for them to support their own weight and the cargo weight during the lifetime of ships and perform their role without breakage by having resistance to stress, corrosion, and fatigue cracking
The completed weldment must be transported to the nondestructive inspection station or the inspection equipment must be installed on site; this process is expensive
According to the above purpose, image preprocessing was performed by applying histogram equalization, denoising, and thresholding
Summary
Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints in order for them to support their own weight and the cargo weight during the lifetime of ships and perform their role without breakage by having resistance to stress, corrosion, and fatigue cracking. Destructive and nondestructive inspection methods have been applied to welding joints. Destructive inspections have high reliability; performing them directly on welding joints is unreasonable. Performing destructive inspections on all joints is difficult. Nondestructive inspections are widely applied during the production process because they can determine pass or fail based on quality standards, by measuring and detecting physical properties that change because of defects without damaging the weld zone. Methods to instantly inspect welding defects on site, while taking into consideration the locations that are difficult for field workers to access, are necessary [1]
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