An adaptive loss weighting multi-task network with attention-guide proposal generation for small size defect inspection

Computer vision-based detection approaches have been widely used in defect inspection tasks. However, identifying small-sized defects is still a challenge for most existing methods. It is mainly because: (1) the existing methods fail to extract sufficient information from the small-sized defects; (2) the existing detectors cannot generate effective region proposals for small-sized defects, which results in a low recall rate. To address the above issues, an adaptive loss weighting multi-task model with attention-guide proposal generation is proposed. First, the proposed multi-task model can excavate contextual information to enrich the feature information of small-sized defect areas, enhancing the model's representation capability. Additionally, to improve the recall rate of small-sized defects, an object attention-guide proposal generation module is proposed by leveraging object attention to guide the confidence enhancement of small-sized defects, which can generate more high-quality region proposals for small-sized defects. Finally, to speed up the joint optimization of the proposed multi-task framework, an adaptive loss weighting algorithm is proposed to learn the optimal combination of multi-task loss functions by maintaining the gradient direction consistency and tuning each task's loss magnitude. The experimental results on the two public defect datasets demonstrate that the proposed method outperforms other state-of-the-art methods.

Automated summary

Computer vision-based detection approaches have been widely used in defect inspection tasks, but identifying small-sized defects remains a challenge. To address this issue, an adaptive loss weighting multi-task model with attention-guide proposal generation is proposed, achieving better results in extracting features from small-sized defects and generating region proposals.