OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection

Abstract

Surface defect detection systems, which have advanced beyond conventional defect detection methods, lower the risk of accidents and increase working efficiency and productivity. Most fault detection techniques demand extra tools, such as ultrasonic sensors or lasers. With the advancements, these techniques can be examined without additional tools. We propose a morphological attention ensemble learning for surface defect detection called OASIS-Net, which can detect defects of three kinds (crack, efflorescence, and spalling) at the bounding box level. Based on the morphological analysis of each defect, OASIS-Net offers specialized loss functions for each defect that can be examined. Specifically, high-frequency image augmentation, connectivity attention, and penalty areas are used to detect cracks. It also compares the colors of the sensing objects and analyzes the image histogram peaks to improve the efflorescence-verification accuracy. Analyzing the ratio of the major and minor axes of the spalling through morphological comparison reveals that the spalling-detection accuracy improved. Defect images are challenging to obtain due to their properties. We labeled some data provided by AI hub and some concrete crack datasets and used them as custom datasets. Finally, an ensemble learning technique based on multi-task classification is suggested to learn and apply the specialized loss of each class to the model. For the custom dataset, the accuracy of the crack detection increased by 5%, the accuracy of the efflorescence detection increased by 4.4%, and the accuracy of the spalling detection increased by 6.6%. The experimental results reveal that the proposed network outperforms the previous state-of-the-art methods.