An effective defect detection method based on improved Generative Adversarial Networks (iGAN) for machined surfaces

In actual industrial applications, the predictive performance of the deep learning model mainly depends on the size and quality of the training samples, while the collection period of defective samples is long or even difficult to obtain. In this article, an effective method based on improved Generative Adversarial Networks (iGAN) is proposed to detect defects of machined surfaces on the basis of positive sample training and image restoration. This model could help to detect surface defects through positive sample learning without the training for defect samples and traditional artificial labels. In this method, an improved reconstructed image network model is constructed based on the Generative Adversarial Networks. Through this method, a defective image could be repaired by determining the threshold of the residual image through the Otsu algorithm, and the difference between the input image and the repaired image will be compared to obtain the defect area. Finally, the experiment for the model validation is conducted on the complex surface image of the engine cylinder head. The result shows that the improved Generative Adversarial Networks is effective in both the image restoration and defect identification process.

Automated summary

The article introduces an effective method based on improved Generative Adversarial Networks for defect detection on machined surfaces using positive sample training and image restoration. By constructing an improved reconstructed image network model and using the Otsu algorithm to determine the threshold of the residual image, this method is able to identify and repair defect areas.