  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.
