# Testing a Vision-Based Autonomous Drone Navigation Model in a Forest Environment
###### Abstract  
Drones play a pivotal role in various industries of Industry 4.0. For achieving the application of drones in a dynamic environment, finding a clear path for their autonomous flight requires more research. This paper addresses the problem of finding a navigation path for an autonomous drone based on visual scene information. A deep learning-based object detection approach can localize obstacles detected in a scene. Considering this approach, we propose a solution framework that includes masking with a color-based segmentation method to identify an empty area where the drone can fly. The scene is described using segmented regions and localization points. The proposed approach can be used to remotely guide drones in dynamic environments that have poor coverage from global positioning systems. The simulation results show that the proposed framework with object detection and the proposed masking technique support drone navigation in a dynamic environment based only on the visual input from the front field of view. deep learning; masking vision; autonomous navigation; drone navigation +
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Currently, forest inspection is conducted mainly via human patrol, which has safety, accuracy, and cost issues [5]. The interruptions and errors in flight control systems can cause severe safety problems, especially in autonomous [5] drone navigation systems. The ability to navigate autonomously is an essential aspect of UAV autonomy. It allows UAVs to autonomously compute the best path from a start point to an end point. UAVs have to constantly change their trajectories depending on the particular terrains and conditions prevailing during their flight [6]. Therefore, a reliable control system is required for a drone to perform complex tasks like autonomous navigation in a dynamic environment without human intervention, which remains challenging. For this purpose, certain variables, such as the environment, objects, hardware, and algorithm, must be considered in developing 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]. Even though algorithms that can develop object detection technology have been explored for drones [8], complex tasks, such as acquiring contextual information from surrounding visual objects to determine a clear navigation path in the scene, have been neglected. Obtaining a high-level semantic description for acquired scenes using only object detection and image processing algorithms is one of the difficulties for autonomous drone flight. Localizing a visual object from a moving camera is always a problem due to the camera movement, which results in a lack of reference points in the scene, affecting object detection and high-level scene modeling [7; 9]. Furthermore, prior knowledge based only on the static context is unsuitable for understanding a scene in a dynamic environment. For example, for autonomous flight among trees in a forest, determining methods to detect drones and avoid trees while flying autonomously along a clear path is the research problem of this paper. For this purpose, object detection and tracking become difficult when distinguishing between moving objects and environmental elements [10; 11]. This paper addresses the problem of building a vision-based navigation model that would be virtually the only option for an autonomous drone to maintain its functionality in the absence of reliable positioning systems and sensors other than a 2D visual camera. We consider a scenario where a drone flies through a forest for an inspection mission.