Abstract
Abstract
Surface defect detection poses a challenging problem that has been addressed for decades. Many of actual solutions are based on deep learning algorithms. However, these algorithms require a large amount of data to train accurate models. This becomes especially problematic for semantic segmentation algorithms, which need labeled datasets at the pixel level, a laborious and time-consuming task. Therefore, this paper proposes a semi-supervised method in which the predictions of an object detector are combined with the segmentation of a zero-shot model, eliminating the need to label a dataset for semantic segmentation. Results are compared with relevant supervised semantic segmentation models, such as UNet and DeepLabv3+. UNet achieves an F1 score of 0.824, while DeepLabv3+ achieves 0.847. The proposed method combining YOLOv8n and Segment Anything Model achieves an F1 score of 0.804. This slight loss of F1 is compensated by the significant time savings, unlike UNet and DeepLabv3+, where labeling of the dataset is necessary.
Funder
Spanish National Plan for Research, Development and Innovation
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献