2 Edge line detection and fitting of crane grab boom

2.1 Edge Line Detection of Crane Boom Based on Hough Transform

In an image, pixels with linear features need to be detected. The commonly used methods are divided into two categories. The first type of method predicts the distribution of pixel points through linear regression, with the least squares method[18] and Ransac [19] line fitting being the most representative. The least squares method is only applicable to a group of pixels with straight line characteristics. Serious deviation of outliers will directly affect the accuracy of linear regression. When used to detect straight lines in Figure 5, only one line with serious deviation can be obtained. Although Ransac line fitting has strong anti-interference ability for outliers, it is only suitable for detecting a group of pixels with line features. The second type of method can perform line detection on any pixel in an image with line features from a global perspective. Hough transform, probabilistic Hough transform, LSD, and other methods can detect global line features, among which Hough transform is the most classic and can be used to detect any shape that can be expressed using mathematical formulas. The principle is to transform points on a specific graph into a parameter space, and obtain a maximum solution based on the vote accumulator in the parameter space. This solution corresponds to the parameter of the desired geometric shape. The transformation principle is shown in Figure 9.
Figure 9. Hough transform principle diagram
According to the principle of Hough transform, the more obvious the line features are, that is, the more pixels in an edge detection image are located on the same line, the higher the linearity of the pixel arrangement, and the more detectable they can be. But usually, due to the continuous movement of the crane’s grab boom, the industrial camera also moves, resulting in constantly changing construction backgrounds on site and the impact of different lighting conditions. It is inevitable to cause some small interfering pixels in the differential grayscale image. Select edge detection images detected in harsh working environments for explanation. As shown in Figure 10. The Hough transform detects straight lines when the corresponding voting thresholds in Figure 10 are 30, 40, and 60, respectively.