Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection
This addresses inconsistent product quality judgments in industrial inspection due to label noise, but it is incremental as it builds on existing noisy label methods for a specific domain.
The paper tackles the problem of noisy labels in surface defect detection caused by ambiguous suspicious regions, proposing a framework that learns trustworthy models by redesigning the loss function and adding a pluggable Bayesian module, resulting in effective, robust, and real-time performance.
In the surface defect detection, there are some suspicious regions that cannot be uniquely classified as abnormal or normal. The annotating of suspicious regions is easily affected by factors such as workers' emotional fluctuations and judgment standard, resulting in noisy labels, which in turn leads to missing and false detections, and ultimately leads to inconsistent judgments of product quality. Unlike the usual noisy labels, the ones used for surface defect detection appear to be inconsistent rather than mislabeled. The noise occurs in almost every label and is difficult to correct or evaluate. In this paper, we proposed a framework that learns trustworthy models from noisy labels for surface defect defection. At first, to avoid the negative impact of noisy labels on the model, we represent the suspicious regions with consistent and precise elements at the pixel-level and redesign the loss function. Secondly, without changing network structure and adding any extra labels, pluggable spatially correlated Bayesian module is proposed. Finally, the defect discrimination confidence is proposed to measure the uncertainty, with which anomalies can be identified as defects. Our results indicate not only the effectiveness of the proposed method in learning from noisy labels, but also robustness and real-time performance.