Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling

arXiv:2406.14582v1 Announce Type: new
Abstract: Deep learning has been constantly improving in recent years and a significant number of researchers have devoted themselves to the research of defect detection algorithms. Detection and recognition of small and complex targets is still a problem that needs to be solved. The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces. During steel strip production mechanical forces and environmental factors …

abstract algorithms arxiv authors cs.cv deep learning defect detection detection faster improving problem recognition research researchers small surface targets type