Abstract:

To solve the problem that AGV obstacle detection algorithm performs poorly under the interference of uneven illumination and background texture in smart factories, this study proposes an improved Canny operator for obstacle detection. The method achieves the optimization of AGV obstacle detection in terms of color space, filtering method, gradient direction and adaptive threshold. Through Lab color space conversion, the b component is extracted and then filtered. The improved median filter and bilateral filter are merged to replace the Gaussian filter in the traditional Canny operator, which reduces the loss of edge details while achieving noise reduction, and improves the speed of the algorithm. The edge information is enhanced by increasing the gradient direction, and adaptive thresholding is obtained using Otsu algorithm. Experiments show that the proposed method can improve the accuracy of edge detection and reduce noise interference, thus achieving stable detection of obstacles.

Key words: AGV, Lab, Canny operator, median filtering, bilateral filtering, gradient orientation, adaptive thresholding, obstacle detection