# Automated Detection of Greenhouse Structures Using Cascade Mask R-CNN
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multirow{2}{*}{**Input**} & **Cascade** & **Mask R-CNN** \\  & & **Mask R-CNNmAP** & **mAP** \\ \hline \multirow{4}{*}{**Transfer Learning**} & Heads & 49.29 & 48.28 \\  & 5+ & 77.11 & 55.08 \\  & 4+ & 78.95 & 74.24 \\  & 3+ & 79.79 & 74.52 \\  & all & 79.96 & 72.64 \\ \hline \multirow{4}{*}{**Learning Rate**} & 0.005 & 67.00 & 59.63 \\  & 0.001 & 75.47 & 66.72 \\  & 0.0001 & 76.23 & 63.18 \\  & 0.0003 & 78.86 & 61.13 \\ \hline \multirow{2}{*}{**Backbone**} & Resnet-50 & 77.32 & 67.94 \\  & Resnet-101 & 78.95 & 70.57 \\ \hline \multirow{4}{*}{**Image Size**} & 256 \(\times\) 256 & 60.62 & 48.68 \\  & 512 \(\times\) 512 & 65.63 & 67.59 \\ \cline{1-1}  & 768 \(\times\) 768 & 76.38 & 70.45 \\ \cline{1-1}  & 1024 \(\times\) 1024 & 76.85 & 70.56 \\ \hline \hline \end{tabular}
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Table 1: Results of the experiment by major hyperparameters. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Hyperparameters**} & **Input** \\ \hline NUM classes & 1 + 2 \\ Step per epoch & 200 \\ Detection min confidence & 0.9 \\ Backbone & Resnet-101 \\ Image resize mode & square \\ Image max dim & 768 \\ Image channel count & 3(RGB) \\ Max GT instances & 70 \\ Train ROIS per image & 256 \\ RP anchor scales & (12, 32, 64, 128, 256) \\ Detection max instances & 50 \\ Learning rate & 0.0003 \\ Weight decay & 0.00003 \\ RPN train anchors per image & 320 \\ Layers & 3+ \\ \hline \hline \end{tabular}
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Table 2: Values of _Cascade Mask R-CNN_ hyperparameters. Based on the hyperparameter values, the performances of the three selected models in the current study were evaluated using all available input datasets. The results presented in Table 4 demonstrate that the proposed Cascade Mask R-CNN model performed better than the baseline Mask R-CNN and the Mask R-CNN models. The performance evaluation results show that our proposed Cascade Mask R-CNN model exhibited better performance compared with the two selected models, with an mAP value of 83.60, compared with 70.77 for the baseline Mask R-CNN, and 81.70 for the Mask R-CNN models. These results show that the proposed Cascade Mask R-CNN model demonstrated superior performance, with an mAP value of 1.9, larger than that of the Mask R-CNN, when we considered hyperparameter tuning and transfer learning, and an mAP value of 12.83, larger than that of the baseline Mask R-CNN model. In addition, this study utilized an alternative metric (i.e., the F1-score) to prove the superiority of the proposed model. The F1-score summarizes both the precision and recall values as the harmonic mean [37]. The proposed Cascade Mask R-CNN model outperformed (i.e., achieved higher F1-scores, by 2.94) the Mask R-CNN with hyperparameter tuning and transfer learning. In comparison with the baseline Mask R-CNN model, the proposed Cascade Mask R-CNN model also outperformed (i.e., a 9.74 higher value). Although the calculation time of the Mask R-CNN is better than the Cascade Mask R-CNN, the Cascade Mask R-CNN for greenhouse detection performed better than the Mask R-CNN in terms of the accuracy. In this subsection, we demonstrate the prediction accuracies of the proposed Cascade Mask R-CNN model in predicting objects using two experiments, and present the results in Figure 3. For the first experiment using the input dataset (Figure 3a), the results presented in Figure 3b demonstrate that two small greenhouses located inside the forests were accurately predicted, which shows that the model exhibited good performance. To assess the performance of our proposed model in a complex landscape, we conducted the second \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **mAP** & **F1-Score** \\ \hline baseline Mask R-CNN & 70.77 & 52.33 \\ \hline Mask R-CNN & 81.70 & 59.13 \\ (hyperparameter tuning and transfer learning) & & \\ \hline Cascade Mask R-CNN & 83.60 & 62.07 \\ (hyperparameter tuning and transfer learning) & & \\ \hline \hline \end{tabular}
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