# Dual-Kernel-Based Aggregated Residual Network for Surface Defect Inspection in Injection
## 5 Conclusions  
In this study, two classification models are proposed using fixed and deformable kernels simultaneously. A product that simultaneously has defects on its surface and a defective shape can be detected effectively by referring to appearance defects, such as foreign matter, color variations on the surface of perfect products, defects in geometrical shape, and defects in which both characteristics occur. The practical designs of the deformable kernel--which has been proven to extract geometrical features effectively--and the fixed kernel are expected to effectively extract both surface and shape defects. The proposed model is robust to rotational or positional changes of the product. As demonstrated in the scaled MINIST dataset [10], a model using a serially designed fixed kernel and deformable kernels is robust to the positional change and distortion of the target characteristics. PE-ResNeXt, the model proposed in this study, applies ResNeXt as a backbone, which is known as a simple structure, and includes three deformable kernel layers at the end of the network to combine classification results. The majority voting method is applied to improve classification accuracy. DF-ResNeXt uses a multimodal network method to classify classes and defects using subframes composed of deformable kernel layers with cropped input images. The proposed model has a deformable kernel layer modified from the fixed kernel layer to compensate for the weakness of the existing CNN, and it combines the classification results. The image cropping and feeding method achieve high detection accuracy with a small network and low complexity. In addition, the proposed model exhibits better performance than the deep CNN model by changing the detailed design of the network. The proposed model is demonstrated and proven to be comparable to the models from existing studies using the DAGM dataset. In the test with DAGM data, the classification accuracies of the PE-ResNeXt and DF-ResNeXt models were 99.9% and 100%, respectively. The proposed models with the same output numbers are superior to FR-CNN with output class 6/2 and DCNN with output class 12. The case study demonstrates that the proposed model is valid for products with defects on the surface or in shape. This research conducts comparative research with previous excellent studies. This study contributes to applying and analyzing models verified with existing open-source datasets to actual products. In addition, this paper suggests one of the solutions to the problem that occurs in real cases. The proposed model also has the advantage of easy modification by adapting pretrained networks. However, the classification accuracy needs to be improved for the model to be applied in actual industrial processes. As opposed to actual product data which have variations, there is a lack of variation in the experimental data, and model verification and accuracy improvement with more experiments need to be performed in future studies. Furthermore, we will conduct further research on process anomaly detection by integrating the proposed model, noise recognition of a machining process using LSTM, and operator and part recognition based on R-CNN. Conceptualization, H.L. and K.R. ; methodology, H.L. ; software, H.L. ; validation, H.L. and K.R. ; formal analysis, H.L. ; investigation, K.R. ; resources, H.L. and K.R. ; data curation, H.S. and K.R. ; writing--Original draft preparation, H.L. ; writing--Review and editing, K.R. ; visualization, H.L. ; supervision, K.R. ; project administration, K.R. ; funding acquisition, K.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Acknowledgments:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No.