## Appendix A \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Weighted**} & \multicolumn{1}{c}{**Weighted**} & \multicolumn{1}{c}{**Weighted**} \\  & & **Recall** & **Precision** & **F-Score** \\ \hline Screen & & & & \\ \hline ResNet50 & 0.951 & 0.951 & 0.952 & 0.951 \\ \hline ResNet152 & 0.947 & 0.947 & 0.947 & 0.947 \\ \hline VGG16 & 0.926 & 0.926 & 0.927 & 0.926 \\ \hline VGG19 & 0.922 & 0.922 & 0.925 & 0.922 \\ \hline InceptionV3 & 0.672 & 0.672 & 0.682 & 0.669 \\ \hline EfficientNetV2B2 & 0.918 & 0.918 & 0.919 & 0.918 \\ \hline EfficientNetV2L & 0.943 & 0.943 & 0.945 & 0.943 \\ \hline EfficientNetV2S & 0.935 & 0.935 & 0.938 & 0.934 \\ \hline EfficientNetV2M & 0.939 & 0.939 & 0.940 & 0.939 \\ \hline EfficientNetB0 & 0.938 & 0.938 & 0.940 & 0.938 \\ \hline Member & & & & \\ \hline ResNet50 & 0.842 & 0.842 & 0.845 & 0.838 \\ \hline ResNet152 & 0.859 & 0.859 & 0.860 & 0.857 \\ \hline VGG16 & 0.819 & 0.819 & 0.825 & 0.818 \\ \hline VGG19 & 0.827 & 0.827 & 0.828 & 0.826 \\ \hline InceptionV3 & 0.654 & 0.654 & 0.643 & 0.637 \\ \hline EfficientNetV2B2 & 0.852 & 0.852 & 0.854 & 0.853 \\ \hline EfficientNetV2L & 0.846 & 0.846 & 0.848 & 0.846 \\ \hline EfficientNetV2S & 0.851 & 0.851 & 0.853 & 0.851 \\ \hline EfficientNetV2M & 0.864 & 0.864 & 0.868 & 0.861 \\ \hline EfficientNetB0 & 0.842 & 0.842 & 0.846 & 0.843 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Full results of CNN models based on validation data. The red color indicates the best models for each object based on the weighted F-score. **Table A1. **_Cont._ \begin{tabular}{c c c c c} \hline \hline  & **Model** & **Accuracy** & \begin{tabular}{c} **Weighted** \\ **Recall** \\ \end{tabular} & \
\begin{tabular}{c} **Weighted** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Weighted** \\ **F-Score** \\ \end{tabular} \\ \hline Pin & & & & & \\ \hline ResNet50 & 0.977 & 0.977 & 0.977 & 0.976 \\ \hline ResNet152 & 0.978 & 0.978 & 0.978 & 0.978 \\ \hline VGG16 & 0.976 & 0.976 & 0.977 & 0.976 \\ \hline VGG19 & 0.974 & 0.974 & 0.974 & 0.974 \\ \hline InceptionV3 & 0.900 & 0.900 & 0.895 & 0.896 \\ \hline EfficientNetV2B2 & 0.966 & 0.966 & 0.966 & 0.966 \\ \hline EfficientNetV2L & 0.970 & 0.970 & 0.970 & 0.970 \\ \hline EfficientNetV2S & 0.977 & 0.977 & 0.977 & 0.977 \\ \hline EfficientNetV2M & 0.961 & 0.961 & 0.962 & 0.961 \\ \hline EfficientNetB0 & 0.973 & 0.973 & 0.975 & 0.973 \\ \hline Property & & & & \\ \hline ResNet50 & 0.839 & 0.839 & 0.835 & 0.835 \\ \hline ResNet152 & 0.852 & 0.852 & 0.850 & 0.847 \\ \hline VGG16 & 0.841 & 0.841 & 0.839 & 0.832 \\ \hline VGG19 & 0.837 & 0.837 & 0.832 & 0.829 \\ \hline InceptionV3 & 0.673 & 0.673 & 0.648 & 0.657 \\ \hline EfficientNetV2B2 & 0.877 & 0.877 & 0.881 & 0.878 \\ \hline EfficientNetV2L & 0.870 & 0.870 & 0.873 & 0.870 \\ \hline EfficientNetV2S & 0.867 & 0.867 & 0.868 & 0.867 \\ \hline EfficientNetV2M & 0.860 & 0.860 & 0.862 & 0.858 \\ \hline EfficientNetB0 & 0.863 & 0.863 & 0.869 & 0.864 \\ \hline Map & & & & \\ \hline ResNet50 & 0.967 & 0.967 & 0.967 & 0.966 \\ \hline ResNet152 & 0.974 & 0.974 & 0.974 & 0.974 \\ \hline VGG16 & 0.971 & 0.971 & 0.971 & 0.971 \\ \hline VGG19 & 0.975 & 0.975 & 0.975 & 0.975 \\ \hline InceptionV3 & 0.903 & 0.903 & 0.903 & 0.898 \\ \hline EfficientNetV2B2 & 0.976 & 0.976 & 0.976 & 0.976 \\ \hline EfficientNetV2L & 0.973 & 0.973 & 0.973 & 0.973 \\ \hline EfficientNetV2S & 0.981 & 0.981 & 0.981 & 0.981 \\ \hline EfficientNetV2M & 0.963 & 0.963 & 0.965 & 0.963 \\ \hline EfficientNetB0 & 0.971 & 0.971 & 0.971 & 0.971 \\ \hline \hline \end{tabular}   
* _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Role of Context for Object Detection and Semantic Segmentation in the Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23-28 June 2014; pp. 891-898. * _[PERSON] et al.