Anchor-based Detection and Height Estimation Framework for Particle Defects on Cathodic Copper Plate Surface

Particle defects on the cathodic copper plate surface always happen due to the immaturity of electrolytic copper processing. The removal of defects mainly depends on their height exceeding the plate and current removal requires manual measurement and operation, which is time-consuming and laborious. To automate the removal process, machine vision-based defect detection methods need to be developed. However, copper defects are of very small size, which increases the difficulty of feature extraction and prediction. Therefore, this paper proposes a novel Anchor-based Detection and Height Estimation (ADHE) framework, to locate the defect out and estimate the height of the defect in an end-to-end way. Large-scale raw images are transformed into several image blocks as input. Defect features are obtained by Defect Region Extraction Network and then sent into Height-RCNN for defect detection and height prediction. Dataset of cathodic copper plate surface defects has been collected from a real-world manufacturing factory. Experimental results show that the proposed ADHE method can effectively address the small size problem of copper defects and achieve excellent results in detection and height estimation.