Trainable guided attention based robust leather defect detection

Abstract

Automatic leather defect detection is becoming increasingly important as a crucial requirement for industry 4.0. It is a highly challenging problem due to the varying appearance and characteristics of various defect types. The diversified scale and spatial position of the defects, and high intra-class variance in addition to inter-class similarity, make the problem highly challenging. A novel optimized backbone network that uses reduced filters to extract global semantic information for defect detection. The feature pyramid network capture defects of different sizes, the spatial attention focuses the detector on precise defect locations, whereas the cross-channel guidance along with channel attention aid the network to discriminate between defects of similar and different classes. The current state bounding box regression methods are employed in conjunction with the proposed OAFE module for precise object localization.