Enhancing Agricultural UAV Safety: A Binocular Stereo Vision System for Cable Detection

ABSTRACT This study develops a binocular stereo vision system to enhance the operational safety of agricultural unmanned aerial vehicles (UAVs) by detecting and localizing thin cables—one of the most prevalent and hazardous obstacles in farmland environments. The system integrates cost‐effective hardware (dual OV4689 CMOS sensors, 36°/8 mm lenses, 120 mm baseline) with a process‐enhanced SemiGlobal Block Matching algorithm, specifically tailored to address the core challenge of low‐contrast cable detection in complex agricultural settings. The algorithmic innovation combines Hue, Saturation, and Lightness color space segmentation with adaptive dynamic threshold processing, effectively resolving cable feature extraction under variable illumination conditions and intense background interference from crops or terrain. Experimental results demonstrate maximum detection distances of 14.547 and 16.846 m for 20 and 40 mm diameter cables, respectively, with an average ranging error of 0.055 m (0.58% error rate). UAV Flight tests confirm reliable obstacle avoidance within a 2–5 m/s velocity range, achieving an average braking distance of 11.828 m—sufficient to ensure safe maneuvering given agricultural UAVs' inherent dynamic constraints of high moment of inertia and low thrust‐to‐weight ratio. Compared with conventional approaches, the proposed system exhibits superior chromatic separation between cables and backgrounds and enhanced dynamic noise suppression, while maintaining low hardware cost. This renders it a highly cost‐efficient and deployable safety solution for agricultural scenarios with dense cable distributions. The research outcomes provide robust technical support for autonomous obstacle avoidance of agricultural UAVs and offer significant practical value for improving operational safety in precision agriculture.