# Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm
## 3 Experimental Results Analysis
### Comparison Experiment
#### 3.3.2 Training Effect of Different Kinds of Stones in the ST Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stone** & **Network Model** & **mAP0.5** & **mAP0.5–0.95** & **_p_** & **R** & **MIoU** \\ \hline \multirow{8}{*}{Pighes White} & U-Net & 0.846 \(\pm\) 0.005 & 0.421 \(\pm\) 0.014 & 95.7 & 74.5 & 81.2 \(\pm\) 0.23 \\  & VNet & 0.825 \(\pm\) 0.014 & 0.412 \(\pm\) 0.016 & 90.5 & 76.6 & 80.8 \(\pm\) 0.32 \\  & AttentionUNet & 0.859 \(\pm\) 0.011 & 0.455 \(\pm\) 0.017 & 94.1 & 80.5 & 83.9 \(\pm\) 0.14 \\  & DeepLabV3 & 0.845 \(\pm\) 0.017 & 0.475 \(\pm\) 0.013 & 95.8 & 76.5 & 84.3 \(\pm\) 0.27 \\  & SegNet & 0.772 \(\pm\) 0.013 & 0.387 \(\pm\) 0.011 & 85.2 & 61.7 & 72.1 \(\pm\) 0.41 \\  & U2 NetP & 0.783 \(\pm\) 0.008 & 0.411 \(\pm\) 0.009 & 89.3 & 69.3 & 78.4 \(\pm\) 0.37 \\  & YOLOv8-Seg & 0.862 \(\pm\) 0.014 & 0.488 \(\pm\) 0.014 & 94.2 & 79.1 & 85.5 \(\pm\) 0.16 \\ \hline \multirow{8}{*}{Hermes Grey} & U-Net & 0.772 \(\pm\) 0.011 & 0.392 \(\pm\) 0.023 & 85.4 & 70.2 & 76.2 \(\pm\) 0.35 \\  & VNet & 0.728 \(\pm\) 0.013 & 0.361 \(\pm\) 0.009 & 83.9 & 6.5 & 73.5 \(\pm\) 0.15 \\  & AttentionUNet & 0.812 \(\pm\) 0.007 & 0.427 \(\pm\) 0.013 & 91.3 & 76.4 & 74.6 \(\pm\) 0.16 \\  & DeepLabV3 & 0.803 \(\pm\) 0.009 & 0.418 \(\pm\) 0.017 & 92.5 & 74.8 & 74.1 \(\pm\) 0.27 \\  & SegNet & 0.584 \(\pm\) 0.017 & 0.231 \(\pm\) 0.013 & 76.8 & 53.1 & 61.3 \(\pm\) 0.29 \\  & U2 NetP & 0.758 \(\pm\) 0.012 & 0.376 \(\pm\) 0.014 & 87.6 & 67.3 & 70.1 \(\pm\) 0.11 \\  & YOLOv8-Seg & 0.828 \(\pm\) 0.009 & 0.448 \(\pm\) 0.011 & 91.8 & 77.9 & 76.8 \(\pm\) 0.27 \\ \hline \multirow{8}{*}{Gialo} & U-Net & 0.693 \(\pm\) 0.014 & 0.387 \(\pm\) 0.013 & 78.9 & 72.3 & 66.2 \(\pm\) 0.37 \\  & VNet & 0.681 \(\pm\) 0.012 & 0.358 \(\pm\) 0.008 & 76.4 & 69.8 & 62.8 \(\pm\) 0.15 \\  & AttentionUNet & 0.729 \(\pm\) 0.007 & 0.401 \(\pm\) 0.013 & 84.3 & 66.5 & 67.6 \(\pm\) 0.22 \\  & DeepLabV3 & 0.716 \(\pm\) 0.008 & 0.403 \(\pm\) 0.019 & 84.2 & 65.1 & 68.1 \(\pm\) 0.27 \\  & SegNet & 0.592 \(\pm\) 0.015 & 0.231 \(\pm\) 0.025 & 70.1 & 54.9 & 61.3 \(\pm\) 0.25 \\  & U2 NetP & 0.685 \(\pm\) 0.024 & 0.393 \(\pm\) 0.014 & 72.5 & 68.3 & 60.1 \(\pm\) 0.16 \\  & YOLOv8-Seg & 0.741 \(\pm\) 0.021 & 0.408 \(\pm\) 0.013 & 83.8 & 68.9 & 69.8 \(\pm\) 0.31 \\ \hline \multirow{8}{*}{Wellington Rocks} & U-Net & 0.784 \(\pm\) 0.016 & 0.403 \(\pm\) 0.013 & 86.2 & 71.5 & 72.9 \(\pm\) 0.19 \\  & VNet & 0.735 \(\pm\) 0.028 & 0.352 \(\pm\) 0.011 & 84.1 & 65.8 & 68.7 \(\pm\) 0.15 \\  & AttentionUNet & 0.823 \(\pm\) 0.011 & 0.438 \(\pm\) 0.008 & 90.7 & 78.3 & 78.5 \(\pm\) 0.27 \\  & DeepLabV3 & 0.807 \(\pm\) 0.026 & 0.427 \(\pm\) 0.009 & 91.9 & 75.6 & 77.8 \(\pm\) 0.23 \\  & SegNet & 0.598 \(\pm\) 0.023 & 0.242 \(\pm\) 0.018 & 77.5 & 54.7 & 58.6 \(\pm\) 0.17 \\  & U2 NetP & 0.746 \(\pm\) 0.014 & 0.368 \(\pm\) 0.006 & 88.3 & 66.9 & 71.0 \(\pm\) 0.25 \\  & YOLOv8-Seg & 0.831 \(\pm\) 0.015 & 0.457 \(\pm\) 0.016 & 90.2 & 77.5 & 79.3 \(\pm\) 0.18 \\ \hline \multirow{8}{*}{Landscape Painting Stone} & U-Net & 0.791 \(\pm\) 0.021 & 0.408 \(\pm\) 0.017 & 87.6 & 72.1 & 70.5 \(\pm\) 0.22 \\  & VNet & 0.741 \(\pm\) 0.007 & 0.371 \(\pm\) 0.016 & 85.4 & 67.2 & 64.3 \(\pm\) 0.10 \\  & AttentionUNet & 0.823 \(\pm\) 0.014 & 0.441 \(\pm\) 0.013 & 92.8 & 77.8 & 75.8 \(\pm\) 0.23 \\  & DeepLabV3 & 0.816 \(\pm\) 0.011 & 0.433 \(\pm\) 0.013 & 93.1 & 76.5 & 75.2 \(\pm\) 0.17 \\  & SegNet & 0.607 \(\pm\) 0.027 & 0.258 \(\pm\) 0.008 & 78.9 & 55.4 & 56.1 \(\pm\) 0.09 \\  & U2 NetP & 0.762 \(\pm\) 0.019 & 0.382 \(\pm\) 0.009 & 89.0 & 68.1 & 68.9 \(\pm\) 0.26 \\  & YOLOv8-Seg & 0.837 \(\pm\) 0.007 & 0.452 \(\pm\) 0.008 & 92.7 & 78.4 & 77.2 \(\pm\) 0.31 \\ \hline \multirow{8}{*}{Pink Quarzite} & U-Net & 0.799 \(\pm\) 0.014 & 0.412 \(\pm\) 0.008 & 88.3 & 73.6 & 73.0 \(\pm\) 0.15 \\  & VNet & 0.753 \(\pm\) 0.018 & 0.366 \(\pm\) 0.011 & 86.7 & 69.4 & 69.1 \(\pm\) 0.19 \\ \cline{1-1}  & AttentionUNet & 0.829 \(\pm\) 0.011 & 0.449 \(\pm\) 0.013 & 93.8 & 78.1 & 78.6 \(\pm\) 0.20 \\ \cline{1-1}  & DeepLabV3 & 0.822 \(\pm\) 0.012 & 0.438 \(\pm\) 0.014 & 94.2 & 77.3 & 76.9 \(\pm\) 0.27 \\ \cline{1-1}  & SegNet & 0.623 \(\pm\) 0.022 & 0.267 \(\pm\) 0.016 & 80.1 & 57.6 & 59.8 \(\pm\) 0.23 \\ \cline{1-1}  & U2 NetP & 0.778 \(\pm\) 0.025 & 0.394 \(\pm\) 0.013 &