## 4 Conclusions  
This paper presented a visualization technique and transfer learning model for sorting anti-loosening coated bolts using deep learning technology. The conclusions of the study are as follows:  
1. Four cameras were used to photograph the entire area of the bolt, and the lighting was set above the bolt such that the coating color could be seen evenly. A jig was used  
Figure 8: System for inspecting bolt coating defects and sorting. NG, defective; OK, non-defective.  
Figure 9: GUI for anti-loosening coating bolt screening. NG, defective product; OK, non-defective product.  
to fix the camera and lighting, and thereby, provide a shooting environment. Using preset defect criteria, the data were binarily classified to configure an image dataset for the learning process.
2. The acquired images were used as input data for the learning process through the CNN VGG16 structure. The accuracy of the coated bolt sorting model was 97%. The CNN VGG16 gradually reduced the pixel values for vectorization; however, location information was lost, making it impossible to determine the standards that the model used to classify the images. Therefore, a visualization technique was used to verify the reliability of the model.
3. Owing to the shortcomings of the visualization technique, in which the classification layer had to be converted to global average pooling, Grad-CAM was used for supplementation. This allowed the visualization of learning images without changing the VGG16's structure. The results indicated that the previously input images were classified based on external factors such as the jigs and cameras, in addition to the bolt thread coating area. Hence, the ROI technique was used to designate the bolt thread as the only area of interest used as input data. The classification proved to be conducted based on the defect location on the bolt thread area, as well as other criteria.
4. The anti-loosening coated bolts used in the current study posed a problem due to limited sample sizes, although larger data sizes would lead to longer learning times. Therefore, to reduce the learning time and improve the model performance, the transfer learning technique was also utilized. Using the weighted values learned using the ImageNet big dataset, the model was taught such that only Block 5 and the classification layer would be appropriate for the coated bolt dataset. The subsequent fine tuning resulted in a model accuracy rate of 98%.
5. To demonstrate that the coated bolt sorting model is applicable to the actual sorting process, input sensors, motors, and cameras were connected to a conveyor belt via a PLC device and the logic configured. The images acquired by the cameras were put through the coated bolt sorting model to identify defective and non-defective products. The values were transmitted using the Modbus TCP method between the PLC and a PC, thereby proving that the model is applicable to the actual sorting process.  
Conceptualization, S.H.; methodology, S.H. and E.N.; software, E.N.; validation, E.N.; investigation, E.N.; writing--original draft preparation, E.N.; writing--review and editing, S.H.; visualization, E.N.; supervision, S.H.; project administration, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004).  
Not applicable.  
The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.