# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
## 2 Related Works  
In unfamiliar environments, achieving vision-based autonomous navigation poses significant challenges due to limited knowledge of the surroundings. Consequently, vehicles can only devise paths that optimize their existing knowledge. However, leveraging scene information obtained from a vision sensor can provide the necessary data for navigating through unknown terrains [12]. In UAV applications, real-time navigation heavily relies on the data collected by vision systems. Object detection stands as a crucial element of autonomous systems, particularly in route and path planning. By identifying obstacles in the vehicle's vicinity, a safe navigation route can be determined. However, detecting small objects within the environment presents a formidable challenge. Despite the availability of various sensors like Radio Detection and Ranging (Radar) and Light Detection and Ranging (LiDAR) for navigation support, their performance often falters due to environmental factors [13]. As a viable alternative, vision-based sensors such as cameras find widespread use in autonomous vehicle navigation. Recent advancements and the evolution of deep learning-based solutions [14] have rendered the navigation of autonomous vehicles achievable. Certain related methods, such as moving object detection and tracking [12; 13], have been applied to solve the problems of video-based tracking. Some drone research [14; 15] uses low-level vision-based solutions to solve object recognition problems. They focus on finding the thresholded color to track visual objects, but this approach has limited application for autonomous drones. Many factors, such as the surrounding objects, obstacles, remote environments, and the absence of GPS signals, should be considered to obtain a precise understanding of a scene. Some variables, such as the environment, objects, hardware, and algorithm, must be considered in the development of a robust autonomous navigation system. Among these variables, the autonomous navigation of drones based on scene-understanding algorithms has received much attention in recent years [7]. Several works have proposed ways of using deep networks for predicting object bounding boxes [16; 17]. Deep networks for object detection, tracking, and scene understanding with drones have the potential to widen the application scope of autonomous drones. However, many existing deep learning-based solutions may not be generalized to challenging scenarios, such as those with occlusions and complex backgrounds. Object detection algorithms are often combined with other image processing techniques to improve their robustness and efficiency in many applications [18]. A sequence of dual background difference methods was proposed to identify the unattended objects in a scene [19]. The algorithm is based on finding the intensity between the current and reference background models within a time period. Similarly, the authors use an object detector as part of the implementation to identify unattended objects in the scene. Hence, combining object detection algorithms with other image processing techniques can be seen as a novel way to improve scene understanding. There are some studies focusing on surveillance and inspection missions using UAVs [20; 21; 22]. Though they consider different problems, enabling autonomous drone navigation still remains the main challenge for the application of UAVs. Autonomous drone navigation has become an essential component of future industrial UAVs. It is essential to design a robust vision-based navigation model for the considered problems in autonomous drone navigation. To acquire a better understanding of the environment, a drone model that can navigate through larger areas was presented in [23]. The reliability of autonomous drone navigation can only be improved by using GPS information which is equipped with a real-time landmark detection system. The 3D position of a UAV should be acquired based on GPS, as mentioned in [24].