Research on defect detection of toy sets based on an improved U-Net

In order to address the problem of low efficiency and accuracy of artificial visual inspection for the quality of toy sets, this paper investigates the defect detection of toy sets based on machine vision. Firstly, an improved U-Net (IU-Net) is developed by introducing the Squeeze-and-Excitation (SE) block into the U-Net and modifying its objective function. Then, a convolutional neural network (CNN) is constructed for feature extraction and defect matching. Finally, a defect detection model for toy sets is created by integrating these two models. In addition, when it comes to a new-variety toy set, the IU-Net model is trained through transfer learning with a small number of new-variety data samples. This can reduce the dependence on new-variety data samples and improve the efficiency of model training process. The experimental results show that the defect detection accuracy of the proposed method is 100% and 99.43% for the whole toy set and single component, respectively, which meets the requirements of industrial automation quality inspection of toy sets.