Abstract
The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV’s mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV’s downwash effect: Static textures- Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures- Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.
Highlights
Unmanned Aerial Vehicles (UAVs) are a topic of interest in several areas
An Field Programmable Gate Array (FPGA)-based real-time tree crown detection approach for large-scale satellite images was proposed in [22] and the results showed a speedup of 18.75 times for a satellite image with a size of 12,188 × 12,576 pixels when compared to a 12-core CPU
The results provided by Sections 2.4.1, 2.4.2 and 2.5, serve as input to the classification algorithm that converts the selected features into output labels
Summary
Unmanned Aerial Vehicles (UAVs) are a topic of interest in several areas Their use is expected to have a great impact on society [1] in the near future. One very important application of Computer Vision and UAVs is to help Unmanned Surface Vehicles (USV), giving them proper information about the terrain types, allowing them to identify where they can navigate, making them autonomous robots. Other algorithms use color information to classify terrains, such as presented in [6], which is able to distinguish four different terrain types within an image. During this process, each channel’s pixel is divided by the square root of its own three channels intensity. Frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can be used in terrain classification
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