"A High-Performance Multitask Approach for Surface Defect Detection Based on CBAM and Atrous Convolution"

Abstract

This paper presents a model for surface defect detection using the convolutional block attention module and atrous 
convolution, in response to the limitations of existing machine vision-based methods, such as their slow development 
cycle, lack of generalizability, and low accuracy. This model integrates the product's surface defect segmentation 
and classification tasks, pools atrous spatial pyramid data to get picture context at different scales, and then reweights 
the network using the convolutional block attention module to better focus on the defect area and discriminate 
extracted features. Also, the deep network now uses atrous convolution, which makes the model easier to employ 
for defect segmentation tasks and improves its real-time performance for defect detection. Results from experiments 
demonstrate that the suggested model outperforms state-of-the-art mainstream surface defect detection approaches 
in terms of accuracy and real-time performance, suggesting that it has broad potential for use in identifying surface 
flaws in industrial goods.