# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 4 Laboratory Experimentation of the Proposed DFC Scheme
### Display Detection through Deep Learning  
In addition to channel coding, deep learning has been used to further improve the BER performance of the scheme. As mentioned before, while capturing a display, the first thing to be taken care of is that the display pixels should be in focus with the camera pixels. However, based on the distance between the display and a camera, the camera may capture the background in addition to the display. These background clutters must be removed from the captured image to detect the display area. Rather than using the image processing techniques [35], we use the deep learning-based object detection scheme to detect our screen. In particular, object detection includes a _classification_ concept that classifies what kind of object it is and a _localization_ concept that finds the position of an object via drawing a bounding box around it. The conventional recurrent-convolutional neural network (R-CNN) [36] is a 2-stage detector that performs region proposal and classification sequentially, which has good performance in object detection accuracy but is too slow. To solve this problem, YOLO (You Only Look Once) [37] has appeared. YOLO, a CNN-based object detection algorithm, is a 1-stage detector that performs region proposal and classification at the same time, with slightly lower accuracy than a 2-stage detector, but higher real-time detection speed. In particular, YOLOv4 improved detection accuracy by more than 10% compared to YOLOv3 using various techniques such as bag-of-freebies, bag-of-specials, and mosaic augmentation. Therefore, we use the YOLOv4 model, which is a faster state-of-the-art ML model for real-time object detection.  
Figure 3: Performance comparison of DFC with and without channel coding for \(256\times 256\) pixel Lena image. The experiment was performed by averaging a total of 10 sets of Lena images in which different random data were embedded.  
The image used in the experiment is an open dataset image provided by Google. This consists of 3000 images--each image containing a display screen. Next, we manually annotate these data using an open-source image labeling software known as Labellmg [38; 39]. At this time, the augmentation did not proceed because it was determined that display is an easy classification class and the already labeled images were sufficient. After dataset annotation, the system based on the YOLOv4 network was trained by using images and the weights of the trained network were saved.  
Figure 4 illustrates the display area detection process using the YOLOv4 object detection model. Figure 4a shows the image captured by the camera. It can be observed that the captured image has background clutters. Figure 4b shows an image classified as a display and marked with a bounding box using the deep learning algorithm. Finally, Figure 4c is the cropped image using the four edge coordinates of the bounding box, thus almost eliminating the background clutters. Note that in a normal DFC scenario, the images can be captured from a long distance, so the captured image has a plurality of mixed objects together. In such cases, if deep learning is used to detect the display area and then correct the distortion, more efficient data demodulation can be achieved.