Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning

Automated surface defect detection is a challenging problem that has attracted major attention for decades. Traditional methods were designed using a pipeline of carefully designed operations. The resulting methods were complex systems, which were difficult to tune and adapt to different problems or data. A new approach to solving this problem has emerged recently: deep learning. This trend is motivated by two main factors: the increasing digitization of society, which makes it possible to record large datasets of labeled samples; and the availability of a large pool of computational resources. This work evaluates state-of-the-art deep-learning methods in object detection and semantic segmentation in the field of automated surface inspection in metals. Images acquired in the industry are affected by the conditions of the environment, including noise, dust, and vibrations, which is an additional challenge. Moreover, industrial inspection requires accuracy, but also robustness and speed. The selected methods are applied to different datasets of images that include the most common defects in metals and the performance is compared in terms of accuracy and speed. Results show exceptional accuracy at a fraction of the required processing time.