## 3 The Proposed Model
### Loss Function
The false detection rate of the original Faster-RCNN model in (d) is 12.12%, the false detection rate of the Tree-CycleGAN model that detects mixed samples after mixed sample training is 7.89%, and the false detection rate of the CycleGAN-TSS model is 5.26%, and our model can reduce the false detection rate to 2.70%; in (i) The false detection rate of the original YOLOv5 model is 19.61%, and the false detection rate of the Tree-CycleGAN model that detects mixed samples after mixed sample training is 10.00%, and the false detection rate of the CycleGAN-TSS model is



For datasets with obvious defect features like (b) and (f), our model combines U-Net and DenseNet to enhance the defect feature representation of the samples and improve the learning ability of the defect detection model for defect features, Class2 and Class6 detection results are shown in Figures 7 and 8. the detection rate of the original YOLOv5 model in (b) is 81.67%, and the detection of the mixed samples after training of the mixed samples Tree. The detection rate of the original YOLOv5 model in (f) is 73.33%, and the detection rate of the Tree-CycleGAN model for detecting mixed samples after training of mixed samples is 91.80%, and our model can improve the detection rate to 97.26%, with a detection rate of 84.38%, and the CycleGAN-TSS model with a false detection rate of 85.48%, our model was able to improve the detection rate to 91.04%. Figure 6: Class 9 test results. The comparison experiments show that our model can generate high-quality defect samples on different types of defect datasets, and mixing and matching real defect samples can further improve the generalization ability and robustness of the defect detection model, which can effectively identify defects with obscure features in complex backgrounds and further enhance the authenticity and diversity of defect features. Subsequent IOU value optimization for our optimal model for actual industrial defect detection, and finally, when the IOU value is 0.15, the false detection rate is 0%, and the average accuracy rate of various data sets reaches 98.73%. The experiments show that the defect samples generated by our model are better than those generated by the current defect sample generation model and can be practically applied to industrial defect detection, which can effectively improve the robustness and generalization of the defect detection model. Figure 7: Class 2 test results. ## 5 Conclusions

In this work, we design PreCaCycleGAN, a model-based image-enhanced adversarial generative network for industrial defect sample augmentation, which aims to address the problem of defect data scarcity in industrial defect detection by current deep learning methods and to improve the generalization and robustness of the defect detection model in real industrial manufacturing. We show that our model uses the DenseNet module to enhance the U-Net network to synthesize defect samples and that the defect samples synthesized on different datasets are more realistic and faithful to the defect samples. We also show that our model uses a dual-branch discriminator and an added damage function to make the defect details more refined. We compare the augmentation effect of PreCaCycleGAN on different detection models with the state-of-the-art industrial defect sample generation models and demonstrate that our model has better defect sample

Figure 8: Class 6 test results. generation diversity. However, our proposed method still has some limitations in practical industrial defect detection applications. On the one hand, it is not very effective in some industrial scenarios with limited resources or high-efficiency requirements. On the other hand, the generated industrial defect samples still need manual annotation.