## 6 CNN-Based Synchronization and Color Pattern Classification
However, the YOLO model used in this study can accurately detect the color barcode area, because it uses color barcode images obtained at various angles and with synthetic data from data augmentation. In addition, even if the yaw angle is different, since the color barcode pattern is configured in various forms and then decoded with the trained CNN model, a good data rate performance can be guaranteed based on the high generalization performance. Figure 12: Achievable data rate performance according to distance between the display and the camera. Data adapted from [20]. 2020 [PERSON] et al. Note that the proposed scheme requires no additional data overhead because the structure of the data packet sent from the display is configured in the same way as in the existing method. Since the existing system uses a heuristic analysis method for the histogram, if the color barcode area becomes distorted, rather than rectangular, detection of the color barcode area is not accurate, and it is difficult to accurately obtain the maximum value for each RGB channel of the histogram. When this technique is applied, even in the worst case, it is necessary to determine heuristic conditions for many cases on color barcode histograms, and thus, the complexity becomes very high. However, our deep learning models are well trained to classify unseen data, supporting a robust generalization performance. Our proposed YOLO model supports real-time object detection functionality, and CNN models for synchronization and data decoding consume a processing time of less than 50 ms. In particular, we constructed a shallow CNN framework with fewer layers and parameters, resulting in a less complex structure and less processing time. ## 8 Conclusions

In this paper, we proposed the DeepCCB-OCC system, which can provide imperceptible data transmission and reliable communication in any environment with an electronic display and a camera by using deep learning models. Instead of the heuristic algorithm used in the existing CCB-OCC system [20], a YOLO detection model and CNN classification models were used to automatically extract the barcode area, synchronize packets, and detect symbols. To extract the pure color barcode area from the captured image obtained from the display, the YOLO detection model was used along with additional image-processing techniques, including differential image and morphological operations. Various received color barcode patterns observed in the received image due to synchronization jitter between the camera and the display were defined. Then, color barcodes were classified with a CNN-based model for packet synchronization and data decoding. Conducting experiments with a commercial display and a smartphone in actual use showed a high ADR performance, compared to the conventional model, at various distances and angles between the display and camera. Conceptualization, M.T.K. and B.W.K. ; methodology, M.T.K. and B.W.K. ; formal analysis, M.T.K. and B.W.K. ; investigation, M.T.K. ; writing--original draft preparation, M.T.K. and B.W.K. ; writing--review and editing, M.T.K. and B.W.K. ; supervision, B.W.K. ; project administration, B.W.K. ; funding acquisition, B.W.K. All authors have read and agreed to the published version of the manuscript. Figure 13: Achievable data rate performance according to yaw angle between the display and the camera. Data adapted from [20]. 2020 [PERSON] et al. **Funding:** This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (2022R1A2B5B01001543). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON] Current status and performance analysis of optical camera communication technologies for 5G networks. _IEEE Access_**2017**, \(5\), 4574-4594.