# Automated Detection of Greenhouse Structures Using Cascade Mask R-CNN
## 2 Background
Similarly, the two-stage SPP-Net model solved the problem of distortion and slower computational speed by adding a spatial pyramid pooling layer to solve the image information loss caused by crop and warp operations in R-CNN. Moreover, the two-stage Fast R-CNN model [15] introduced regional feature extraction ideas to solve the problem of multi-stage pipelines, which was a bottleneck of existing R-CNN and CNN methods. The Faster R-CNN model [21] showed an unprecedented speed improvement by introducing RPN to the Fast R-CNN model. Since then, numerous studies based on faster R-CNN have been conducted to solve various problems in the models to address the current limitations in their computational speed. The R-FCN model [22] was proposed along these lines, which effectively solved the translation invariance dilemma through a position-sensitivity score and pooling by encoding the location information of objects by class. In contrast, recently, the Mask R-CNN model [23] replaced Region of Interest pooling (Rol pooling) with Region of Interest Alignment (Rol-Align) to estimate the exact location of the object on the Faster R-CNN for instance segmentation, and maintained a better trade-off between computational speed and accuracy. To simultaneously improve computational speed and accuracy, researchers have recently proposed multi-stage algorithms, such as Cascade object detection methods,in which the algorithms mentioned above are modeled in multi-stage settings. For example, the MR-CNN model [24] was proposed as an iterative region adaptation module to create a more accurate bounding box in a multi-stage structure. Similarly, the Attention-net model [25] was proposed as an alternative to sequential localization of bounding boxes of detected objects, which repeatedly used the same regression to locate them accurately. Moreover, [PERSON] et al. [26] proposed moving the face area of the ground truth to the closest location by adjusting the face candidate area through a CNN of the Cascade structure for accurate face detection. However, in the case of existing object detection methods, useful information in the bounding box with an Intersections over Union (IoU) value of less than 0.5 was not utilized because the IoU was generally set to a value of 0.5 or higher than 0.5. The Cascade R-CNN identified this problem. Object detection models with different IoU criteria were merged into Cascade structures to utilize the information from the low IoU bounding box, which verified performance improvements for Cascade structures with different IoU criteria [27]. In contrast, the HTC model [28] modified the object detection and object segmentation layers connected in parallel to the Cascade R-CNN into a cross-array structure. This approach improved the information flow connecting the mask features extracted during each stage. Similarly, ref. [29] demonstrated the high performance of their proposed multi-stage models by combining bi-directional pyramid networks (BPN) and three IoU learning steps with the SSD structure. Notably, all of these studies used the Cascade structure to achieve high-quality detection. Therefore, considering the potential of the Cascade-based structure, in this study, we aimed to detect houses and greenhouses in various environmental settings prevalent in aerial images by investigating the proposed optimized Cascade Mask R-CNN model. In this study, hyperparameter tuning and transfer learning were performed to optimize the Cascade Mask R-CNN model to detect and optimize greenhouses using 2054 aerial images. The results obtained from the Cascade Mask R-CNN were further compared with the baseline Mask R-CNN and Mask R-CNN to scrutinize the accuracy of our optimized Cascade Mask R-CNN model. In addition, the recent advancements in sensor and data processing technologies result in better depth maps in conjunction with RGB information to facilitate improved object detection [30; 31; 32].