The Comparative Study Between the Grid Size of Image Frame Using Image Subtraction and Pixel Expansion Cue for Free Obstacle Region Detection

Abstract

This research investigates optimizing grid sizes for obstacle detection in Unmanned Aerial Vehicles (UAVs) using vision-based approaches. There were many proper computer visions algorithm that can be used to detect free obstacle region. One of it is by using grid-based approach. By dividing the image that taken by camera, the algorithm can choose the best path to go through. This study will explore various grid sizes, from 3x4 to 24x32, to determine their effectiveness in detecting free obstacle regions in different environments. The methodology combines image subtraction, pixel expansion cues, and k-means segmentation, with images resized to 640x360 pixels and processed to display only two colors for simplification. The experiments, conducted using an iPhone 12 camera and analyzed with Spyder software and OpenCV, reveal that larger grids are more accurate in complex environments, albeit with higher computational time, whereas smaller grids are more efficient in simpler settings. The research identifies a grid size range of 12x16 to 16x24 as a balanced solution for various scenarios, highlighting the need for adaptive sizing and sophisticated algorithms in UAV obstacle detection.