## 5 Empirical Study  
The produced labels were represented by the class ID and point coordinates of the visual landmark in the \([x,y,x+w,y+h]\) format, where \(w\) and \(h\) are the width and height of the bounding box, respectively, \(x\) is the initial coordinate of the bounding box on the horizontal axis, and \(y\) is the initial coordinate of the bounding box on the vertical axis. As the employed ML model was initially pre-trained on the MS COCO dataset [34], we only re-trained the fully connected layer, which used the Softmax activation function. As a computing device for training, we employed the Nvidia Jetson AGX Xavier [36]. This device is known for its low power consumption of only 10W, which allows it to be employed in autonomous mobile robotic devices. The device makes it possible to utilize CUDA cores for real-time calculations. The employed device possesses the following technical characteristics:  
* AI performance: 32 TOPS (trillion operations per second);
* GPU: Nvidia Volta architecture with 512 Nvidia CUDA cores and 64 tensor cores;
* CPU: 8-core 64-bit Nvidia Carmel processor with Armv8.2 architecture, 8 MB L2, and 4 MB L3;
* Deep learning accelerator: 2 NVDLA Engines;
* Computer vision accelerator: 2 PVA accelerators. For the ML model performance evaluation metric, we employed Average Precision (_AP_), which incorporates precision and recall and is usually employed for evaluating ML performance [37]. _AP_ can be characterized as the area under the precision-recall curve, which shows how the accuracy and completeness of the algorithm change at various classification probability thresholds. The IoU is a metric that is usually employed to evaluate the object detection algorithm's performance. Typical threshold values indicate correct detection when the IoU is more than 50%. The IoU value is calculated as the relationship between the intersection areas of the predicted bounding box and the actual ground truth bounding box. Since we are evaluating accuracy for the detection and classification algorithm, we also employed the mean _AP_ (_mAP_) metric, which can be calculated by Equation (1):  
\[mAP=\sum_{q=1}^{Q}AP(q), \tag{1}\]  
where \(Q\) is the number of classes; \(q\) is the class; and \(AP(q)\) is the _AP_ value for the \(q\)'s class. To select an ML model architecture for our use case, we performed an ablation study where we tested how various ML model architectures performed on a testing set of the produced data samples. We tested ML architectures based on a CNN (YOLOv4); Haar cascades; and based on ML with a histogram of directional gradients (HOG) used as a descriptor for a support vector machine (SVM). We pre-trained these models on the same cohort of the produced data and then tested them over the prepared testing set. We employed ML accuracy as the evaluation metric for this ablation study. The ML performance results are presented in Table 1. According to the results, the YOLOv4 object detector that is based on CNN architecture demonstrated the highest accuracy in the studied conditions. Based on these results, we selected the YOLOv4 as an image processor for the designed AV computer-vision-based localization system. Furthermore, the choice of the YOLOv4 was justified by the fact that, at the time of the research, this architecture was recommended for achieving robust real-time computation performance using the employed Nvidia Jetson AGX Xavier computational device. Initially, we re-trained our model for 2000 epochs on the images without affine transformations and tested it on the produced images of the artificial visual landmark captured from the varied distances (from 5 to 20 m) and affected by various environmental conditions. The first column in Table 2 represents the performance results achieved by this model. As one can see, the model poorly detected the target objects in the images and provided unacceptable performance. We then continued to further re-train the model, but, in this case, it was only trained on the images produced with affine transformations.