Straightness monitoring of scraper conveyor based on CUDA-accelerated dynamic programming and optimized panoramic stitching

To enhance the accuracy and real-time performance of straightness monitoring for scraper conveyors in complex and harsh underground environments, a straightness monitoring method for scraper conveyors based on compute unified device architecture (CUDA)-accelerated dynamic programming and optimized panoramic stitching was proposed. First, the frame rates and resolution parameters of images captured by two synchronized cameras were aligned, and the input video stream underwent dark channel enhancement to eliminate the interference of coal dust, water mist, and other factors underground. Then, the oriented FAST and rotated BRIEF (ORB) algorithm was used to detect and calculate feature points and descriptors from the two video frames. Feature point matches were calculated using K-nearest neighbors (KNN), with incorrect matches filtered using a threshold ratio. A homography matrix for image perspective transformation was calculated using the random sample consensus (RANSAC) algorithm. CUDA was employed to accelerate the processing by assigning tasks such as Sobel operator reading, gradient computation, total energy difference calculation, loop initialization of weights and paths, and optimal seam finding to different threads. Kernel functions for energy map computation and seamline optimization were defined, enabling the seamless fusion of the two images along the seamline to complete panoramic stitching. Finally, the Hough transform was applied to perform linear fitting of the coal blocking plate in the middle trough of the stitched panoramic image. The fitted line was superimposed on the panoramic image to reflect the straightness status of the scraper conveyor. The experiment and test results showed that the CUDA-accelerated dynamic programming significantly reduced visible stitching artifacts while ensuring high processing speed. The straight line fitted by the Hough transform closely matched the actual straightness of the scraper conveyor, demonstrating its effectiveness for straightness monitoring of scraper conveyors.