# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 3 Description of the Proposed System Architecture
### Receiver
Through the implementation of novel convolutional techniques and enhancements in data mosaics, YOLOv8 provides a valuable contribution to addressing the need for reliable and responsive object detection. The YOLOv8 model introduces novel convolutional techniques, anchor-free detection, and mosaic data improvement [38; 39; 40]. In Figure 3a, a novel \(3\times 3\) convolution operation is executed, resulting in the replacement of C2f with C3. In the C2f model, the aggregated outputs of all bottleneck layers are employed, but in the C3 model, just the output from the final bottleneck layer is utilized. This approach leads to a reduction in the overall number of parameters and the size of the tensor, hence enhancing the efficiency of convolutional operations employed in the model. Previous YOLO version implemented anchor box method, as illustrated in Figure 3b and can be formulated as follows:  
\[b_{x}=\sigma(t_{x})+k_{x} \tag{1}\]  
Figure 3: **(a)** Replacement of C2f module with C3 module in YOLOv8; **(b)** Visualization of an anchor box in previous YOLO version. \[b_{y}=\sigma(t_{y})+k_{y} \tag{2}\]  
\[b_{w}=p_{w}\times e^{w} \tag{3}\]  
\[b_{h}=p_{h}\times e^{h} \tag{4}\]  
where the variables \(b_{x}\), \(b_{y}\), \(b_{w}\), and \(b_{h}\) represent the centroid, width, and height of the predicted Bounding Box (BB). Additionally, the variables \(t_{x}\), \(t_{y}\), \(t_{w}\), and \(t_{h}\) denote the output of a neural network. Furthermore, the variables \(k_{x}\) and \(k_{y}\) represent the coordinates of the upper left corner of the anchor box, and \(p_{w}\) and \(p_{h}\) denote the width and height of said anchor box. \(e^{w}\) and \(e^{h}\) represent a logarithmic space transformation. Unlike previous YOLO, the YOLOv8 introduces anchor-free detection that can directly estimate the centroid of the object, resulting in a reduction in the number of square predictions. The utilization of this technique has the potential to accelerate the process of Non-Maximum Suppression (NMS), thereby improving the mean Average Precision (mAP) of object detection. Previous models in the field of YOLO utilized a complex anchor box mechanism, which led to a decrease in processing speed for object centre detection and a logarithmic space transformation is employed to refine the precise height and width of the bounding box. In this particular case, YOLOv8 utilizes the sigmoid function to generate (\(t_{x}\), \(t_{y}\)), which represents the centre bounding box coordinates (\(b_{x}\), \(b_{y}\)). Furthermore, a novel approach called the mosaic data enhancement technique was developed, wherein many items are merged into a single entity and afterwards utilized as input for the model. This strategy facilitates the model's ability to identify and analyze different item placements, resulting in a decrease in the required training time for the given dataset. The YOLOv8 architecture includes four depth-specific variants: YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Specifically, variants with a deeper backbone network produce more feature maps, but at the cost of increased computational complexity. In this study, we analyzed and selected the structure with the smallest weight in order to ensure LED detection and tracking accuracy standards and device compatibility. LED segmentation is the subsequent stage. The conventional threshold value approach, which utilizes one or more thresholds, is a simple image segmentation method. Basic thresholding replaces pixel values that are greater than, equal to, or smaller than the threshold \(T\) value. However, this approach is a global thresholding technique, which means that the same value of \(T\) is used to evaluate and classify all pixels in the input image as foreground or background. The issue is that a single \(T\) value may not be adequate in this situation. One value of \(T\) may work for one segment of the input image but fail catastrophically for another due to variations in lighting conditions, shadowing, and other factors.