## 4 Experimental Results  
In the design of the robotic workplace, two distinct computer vision models with varying training parameters were employed. Both models yielded suboptimal results, each exhibiting specific advantages and limitations. YOLOv8 demonstrated superior tool detection and greater robustness to varying tool rotation angles but was characterized by inaccurate segmentation of the detected tools. In contrast, YOLACT provided more precise segmentation of the tools but exhibited poor performance with respect to tool rotation angles. The issues encountered in calculating the take-off position of the tool and evaluating its rotation were likely due to an inadequate dataset size and suboptimal selection of the computer vision model. To provide a more comprehensive evaluation of the proposed approach, additional experimental results are presented in Appendices A and B. To accurately assess the performance of various models, YOLACT and YOLOv8 were compared using a consistent dataset and identical initial conditions. YOLOv8 model was trained for 50 epochs. Due to constraints with YOLACT, training was conducted for 55 epochs. Initially, a qualitative evaluation of the trained models was performed. Each tool was rotated five times by approximately 45 degrees on the workbench, while the camera, providing input to the models, remained stationary. The ability of each model to detect tools in different orientations and the accuracy of the generated masks were qualitatively assessed. The process of tool rotation is illustrated in Figure 6.  
The qualitative assessment indicates that YOLOv8 outperforms other models for the specific application. YOLACT, in contrast, demonstrated significant limitations, particularly in its sensitivity to tool rotation on the workbench, leading to unreliable detection. Although YOLOv5 was initially trained, it exhibited notable issues with detecting a flat screwdriver, often misidentifying it as a Phillips screwdriver, and also performed poorly in image segmentation. The results suggest that with an adequately large and high-quality dataset, YOLOv8 could be trained to achieve reliable tool rotation calculations on the workbench. This improvement would enable the robot to consistently and accurately pick up and deliver the correctly oriented tool to the operator. A comparison of the segmentation results for the trained models is presented in Figure 7. Confusion matrix for the YOLOv8 model is shown in Figure 8 and hyperparameters used during training are shown in Table 2. The final decision to implement YOLOv8 was based on its superior performance compared to YOLOv5, YOLOv7, and YOLACT, as demonstrated in our evaluation. Among the tested models, YOLOv8 achieved the best results for object segmentation, as illustrated in Figure 7. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline lr0 (Initial learning rate) & 0.01 \\ lrf (Final learning rate) & 0.01 \\ momentum (SGD momentum factor) & 0.937 \\ weight decay (L2 regularization factor) & 0.0005 \\ warmup epochs (No. of warmup epochs) & 3.0 \\ warmup momentum (Initial warmup momentum) & 0.8 \\ box (Bounding box loss weigh) & 7.5 \\ cls (Classification loss weight) & 0.5 \\ dfl (Distribution Focal Loss gain) & 1.5 \\ hsv h (Random hue augmentation) & 0.015 \\ hsv s (Random saturation augmentation) & 0.7 \\ hsv vs. (Random brightness augmentation) & 0.4 \\ degrees (Maximum rotation augmentation) & 0.0 \\ translate (Maximum translation augmentation) & 0.1 \\ scale (Random scaling augmentation ) & 0.5 \\ shear (Maximum shear augmentation) & 0.0 \\ perspective (Random perspective augmentation) & 0.0 \\ flipud (Prob. of vertical image flip) & 0.0 \\ fliplr (Prob. of horizontal image flip) & 0.5 \\ bgr (BGR augmentation) & 0.0 \\ mosaic (Prob. of using mosaic augmentation) & 1.0 \\ mixup (Prob. of using mixup augmentation) & 0.0 \\ copy paste (Prob. of copy-paste augmentation) & 0.0 \\ \hline \hline \end{tabular} Additional hyperparameters were kept at their default values if not listed in the table.  
\end{table}