# Automated Detection of Greenhouse Structures Using Cascade Mask R-CNN
## 5 Conclusions and Future Prospects  
For the protection, restoration, and advancement of the sustainable use of land and ecosystems, the efficient management of restricted development zones is essential, and is among the Sustainable Development Goals established by the United Nations. This study has provided an optimized and improved deep-learning-based Cascade Mask R-CNN model designed to detect greenhouse facilities automatically. For this purpose, the present work has investigated 2054 aerial images of three large cities (Hanam, Hwaseong, and Gunpo) in the Republic of Korea. Our Cascade Mask R-CNN model was optimized by integrating transfer learning and hyperparameter tuning based on a set of randomly selected aerial images comprising 80% of the original dataset. Figure 4: The model results before and after the experiment. (**a**) Annotations in the original image before the experiment. (**b**) Mask and bounding box of detected objects after applying the mask R-CNN model with hyperparameter tuning and transfer learning. (**c**) Mask and bounding box of detected objects after applying the Cascade Mask R-CNN model. Figure 5: The model results before and after the experiment. (**a**) Annotations in the original image before the experiment. (**b**) Mask and bounding box of detected objects after applying the Cascade Mask R-CNN model. The results obtained from our proposed model have been evaluated compared with those of two existing models (baseline Mask R-CNN and Mask R-CNN) using statistical indicators such as mean Average Precision (mAP). The results show that our proposed model obtained an mAP value of 83.60, thus outperforming the baseline Mask R-CNN model, which had a lower mAP value of 70.77, and the Mask R-CNN, with an mAP value of 81.70. In terms of F1-score, our proposed model obtains a value of 62.07, outperforming the baseline Mask R-CNN model, with a lower F1-score value of 52.33, and the Mask R-CNN model, with an F1-score value of 59.13. Although the operation time of the Mask R-CNN is slightly better than the Cascade Mask R-CNN, the accuracy of the Cascade Mask R-CNN is better than the Mask R-CNN, which we have reported in the current study. This study aims to detect illegal encroachments, such as greenhouses, in the restricted development zones. Therefore, it is crucial to detect greenhouses with better prediction accuracy through routine screening procedures regardless of the increased operation time. The superior performance of our proposed Cascade Mask R-CNN model is attributed to the localization scheme adopted, as well as to instrument segmentation and the optimization of the model for the selected study area. We noticed a slight deterioration in the performance of our proposed model, which is attributed to the instance class ambiguity problem, which is related to the horizontally-oriented bounding box structure used during the training stage. Future works should focus on incorporating rotated bounding boxes into the structure of the object detection pipeline to address this limitation and further improve the performance of the proposed Cascade Mask R-CNN model. Further, recently, new approaches for object detection have been proposed. For example, training a model by separating a training sample by considering the different sensitivities of classification and regression [40] or segmenting instances and inferring relative importance rankings [41] have been proposed. Further investigation should focus on the design of a robust algorithm for detecting greenhouses accurately. The optimized Cascade Mask R-CNN model proposed in this study is expected to facilitate the automatic and accurate detection of greenhouses and similar structures, as well as the development of plans to control illegal encroachments, which will provide policymakers with a baseline for the sustainable development of restricted zones in the Republic of Korea, as well as similar regions across the globe. Conceptualization, H.Y.O. ; methodology, H.Y.O., M.S.K., S.B.J. and M.-H.J. ; software, H.Y.O., M.S.K., S.B.J. and M.-H.J.