An Efficient Rail Recognition Scheme Used for Piloting Mini Autonomous UAV in Railway Inspection

摘要

In this paper, a rail recognition scheme is presented to pilot UAV autonomous flying along the track rail. Firstly, the pulse coupled neural network is used to iteratively process the single-channel brightness image and the binary image of the track contour is obtained. Here, the image entropy is adopted as the judgment basis for the stop of iterative processing. Then, the third order Bézier curve is used to fit the contour, and the vanishing point is obtained. Based on the vanishing point and the inverse perspective transformation method, the local flight target point is calculated. Then the yaw and pitch are calculated based on the vanishing point for the next step of inverse perspective transformation to calculate the local flight target point. Finally, the angle between flight direction and target point is calculated to pilot the UAV flying along the track. To speed the processing, CUDA based parallel programming in NVIDIA TX2 is adopted. In the end, various scene images, including forward flight, side flight, height change of UAV and the influence of invasion objects, are used to test the effective of the scheme presented in this paper. Experiments show that the rail recognition rate is 96.08% and the false alarm rate is 1.80%.