Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Active Stereo Vision

Guocai Zhang, Guixiong Liu, Fei Zhong

This study proposes an autonomous recognition and approach method for unmanned aerial vehicles (UAVs) targeting linear splicing sleeves. By integrating deep learning and active stereo vision, this method addresses the navigation challenges faced by UAVs during the identification, localization, and docking of splicing sleeves on overhead power transmission lines. First, a two-stage localization strategy, LC (Local Clustering)-RB (Reparameterization Block)-YOLO (You Only Look Once)v8n (OBB (Oriented Bounding Box)), is developed for linear target splicing sleeves. This strategy ensures rapid, accurate, and reliable recognition and localization while generating precise waypoints for UAV docking with splicing sleeves. Next, virtual reality technology is utilized to expand the splicing sleeve dataset, creating the DSS dataset tailored to diverse scenarios. This enhancement improves the robustness and generalization capability of the recognition model. Finally, a UAV approach splicing sleeve (UAV-ASS) visual navigation simulation platform is developed using the Robot Operating System (ROS), the PX4 open-source flight control system, and the GAZEBO 3D robotics simulator. This platform simulates the UAV’s final approach to the splicing sleeves. Experimental results demonstrate that, on the DSS dataset, the RB-YOLOv8n(OBB) model achieves a mean average precision (mAP0.5) of 96.4%, with an image inference speed of 86.41 frames per second. By incorporating the LC-based fine localization method, the five rotational bounding box parameters (x, y, w, h, and angle) of the splicing sleeve achieve a mean relative error (MRE) ranging from 3.39% to 4.21%. Additionally, the correlation coefficients (ρ) with manually annotated positions improve to 0.99, 0.99, 0.98, 0.95, and 0.98, respectively. These improvements significantly enhance the accuracy and stability of splicing sleeve localization. Moreover, the developed UAV-ASS visual navigation simulation platform effectively validates high-risk algorithms for UAV autonomous recognition and docking with splicing sleeves on power transmission lines, reducing testing costs and associated safety risks.