# Automated Detection of Greenhouse Structures Using Cascade Mask R-CNN
###### Abstract  
Automated detection of the content of images remains a challenging problem in artificial intelligence. Hence, continuous manual monitoring of restricted development zones is critical to maintaining territorial integrity and national security. In this regard, local governments of the Republic of Korea conduct four periodic inspections per year to preserve national territories from illegal encroachments and unauthorized developments in restricted zones. The considerable expense makes responding to illegal developments difficult for local governments. To address this challenge, we propose a deep-learning-based Cascade Mask region-based convolutional neural network (R-CNN) algorithm designed to perform automated detection of greenhouses in aerial photographs for efficient and continuous monitoring of restricted development zones in the Republic of Korea. Our proposed model is regional-based because it was optimized for the Republic of Korea via transfer learning and hyperparameter tuning, which improved the efficiency of the automated detection of greenhouse facilities. The experimental results demonstrated that the mAP value of the proposed Cascade Mask R-CNN model was 83.6, which was 12.83 higher than baseline mask R-CNN, and 0.9 higher than Mask R-CNN with hyperparameter tuning and transfer learning considered. Similarly, the F1-score of the proposed Cascade Mask R-CNN model was 62.07, which outperformed those of the baseline mask R-CNN and the Mask R-CNN with hyperparameter tuning and transfer learning considered (i.e., the F1-score 52.33 and 59.13, respectively). The proposed improved Cascade Mask R-CNN model is expected to facilitate efficient and continuous monitoring of restricted development zones through routine screening procedures. Moreover, this work provides a baseline for developing an integrated management system for national-scale land-use planning and development infrastructure by synergizing geographical information systems, remote sensing, and deep learning models. deep learning; computer vision; object detection; instance segmentation; aerial photograph 20222  
Alongside recent advancements in computing technology, cutting-edge research on machine learning has been actively conducted in various fields, such as computer vision and natural language processing. In the case of computer vision, object extraction methodologies such as You Only Look Once (YOLO)-based models [2; 3; 4; 5], and deep learning-based convolutional neural networks are rapidly being developed. These methodologies have been applied to datasets of images collected from satellite, aerial, and drone-based platforms to detect objects and record their information in a geospatial database for real-time object detection and monitoring. In the case of object detection, these methodologies have been extended to restricted development zones to perform automatic detection and monitoring. They can facilitate the continuous monitoring of restricted zones and enforcement actions against illegal developments and land encroachments. Therefore, the primary purpose of this study is to improve the initial sensing stage of object detection methods through object recognition, and to ascertain the status of the artificial building and greenhouse structures in restricted development zones. To achieve this goal, we integrated a deep learning-based Cascade Mask region-based convolutional neural network (Cascade Mask R-CNN) model [6]. This study used a total of 2054 selected aerial images obtained from three cities (Hanam, Hwaseong, and Gunpo) in the Republic of Korea. Here, the data used in this study consist primarily of information obtained from the RGB bands. Previous literature studies reveal that, in order to detect objects from images accurately, YOLO is applied to thermal images [7]. In contrast, other studies have integrated both RGB and thermal images to detect objects [8].