Development of image-based fast defect recognition and localization network (FDRLNet) for steel surfaces

Abstract

The utilization of vision-based systems for automated inspection and quality control tasks increased in recent years due to the adaptation of Industry 4.0 initiatives in manufacturing. Continuous steel production units extensively employ vision-based solutions to classify and localize surface defects. The inspection system must provide quick and reliable feedback about surface defect type (classification) and location (localization) utilizing images acquired from the camera. This paper presents a novel surface defect detector, Fast Defect Recognition and Localization Network (FDRLNet), for achieving accurate prediction abilities at higher inference speeds. The robustness of the proposed approach is validated by utilizing publicly available Northeastern University (NEU-DET) surface defect dataset. The prediction abilities are corroborated by comparing the mean Average Precision (mAP) and inference speed with other competitive approaches presented in the literature. It has been shown that the proposed approach has robust prediction abilities at higher inference speeds and can be implemented in real-time defect detection tasks.