In product quality monitoring, surface defect detection (SDD) is an important part related to the appearance and performance of the product. Machine vision-based SDD usually needs to construct a product-specific detection model and manually label the dataset for training model by paid labeler. We try to transform the single labeler’s work into collaborative labeling works of multiple Internet users to reduce the labeling cost, and propose CrowdLab, a crowd-sourcing dataset labeling system for SDD. In CrowdLab, we tactfully segment the product surface image and employ verification image in web login to make multiple users collaboratively select the part of defect regions and finally recognize these regions. The experiment results illustrate that SDD model using CrowdLab dataset can achieve 96.7% accuracy, which is close to the personnel work.