# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
## 2 Related Works
Consequently, it is easy to localize a target based on the UAV camera information, such as the position and orientation [25]. However, GPS information is not always available due to certain obstacles for GPS signals, such as trees, buildings and adverse atmospheric conditions. Semantic segmentation has been widely used in automated driving [26]. However, implementing semantic segmentation in a safety system environment that requires high levels of accuracy and robustness is a challenge. Nevertheless, semantic segmentation is useful for dividing an image or scene into different parts [27], and can be applied to an image consisting of unknown objects to find the overall region of interest in the scene. The recent advances in object detection have been driven by the success of region proposal methods [28] and region-based convolutional neural networks (R-CNNs) [29]. In brief, an R-CNN is a combination of a region proposal network (RPN) and a convolutional neural network (CNN). A CNN is mainly used for image classification, while an RPN is used for generating region proposals. [PERSON] et al. [30] proposed an object detection system with an R-CNN architecture. The R-CNN architecture consists of three modules. The first module generates region proposals to determine the corresponding features in the region, the second module uses a CNN to extract features from the proposals, and the third module adopts a class-specific linear support vector machine for object classification. Various works demonstrate that deep network-based region proposal methods outperform certain previous works [28; 29]. The state-of-the-art object proposal method, namely, Faster R-CNN [31], requires 300 proposals compared with the 2000 proposals produced by [32]. For certain reasons, R-CNN requires a forward pass of the CNN for every region proposal for every image. Additionally, R-CNN requires separate training for three different models, which makes it rather slow for object detection. Hence, Fast R-CNN [33] and Faster R-CNN [34] were proposed to solve these problems. In the RPN of Faster R-CNN, the time cost of generating region proposals is much shorter than that of Fast R-CNN [31]. Real-time and cloud-based object detection for UAV- and drone-based object counting by a spatially regularized regional proposal network has shown impressive results in detecting different types of objects in real time. However, these region proposal drone-based object detection methods use a top-view perspective, which has no obstacles in its field of view. In a scenario where the drone operates in a remote environment with many background objects in the front view of the drone camera, the spatial relationships between the detected objects should be described to enable precise navigation of the drone. The primary goal of our approach is to build an object detection algorithm by applying a state-of-the-art detector [31], combined with our proposed masking technique, to semantically interpret the scene. The semantic technologies facilitate building a high-level description of the environment and its elements based on the heterogeneous information gathered from different sources, unlike the drone-based solutions mentioned above, which use low-level image processing techniques and heavily rely on a GPS for detecting environmental features.