Example (c) Example (d)

Figure 18. Test Example Diagram

In example (a), the Hough transform threshold T is taken as 30. There are many interfering pixels on the left side of the edge detection image, and there are many non real edge detection lines in the lines detected by the Hough transform. The original k-means method directly clusters the Hough transform results and takes the cluster centroid as the basis for line fitting. In example (a), the method in this article takes m=1, which is a standard deviation. After clustering is completed, the threshold is adaptively calculated. The method in this article preserves reliable straight lines, and then recalculates the clustering centroid to replace the original k-means clustering centroid. From the detection results graph, it can be intuitively seen that the fitting line of this method is more accurate in areas with more interference than the fitting line of the original k-means method.

In example (b), there are more interfering pixels on the left side of the edge detection image, making the edge image more noisy and visible to the human eye. Due to the further increase in interfering pixels, the error of the original k-means clustering method further increases, resulting in complete detection failure. Under such interference conditions, the method in this article can still detect the straight lines we need.

In example (c), the edge detection result graph shows that there is interference edge at a distance far from the crane to grab the boom. Similarly, the original k-means detection result produces a significant error.

In example (d), there are no other interfering pixels on the four edges of the crane grabbing the boom in the edge detection image. In such detection situations, the detection performance of our method is basically consistent with the original k-means clustering method.

These four sets of examples demonstrate that for edge detection images with different levels of interference, the detection performance of our method is superior to the original k-means clustering method, with higher accuracy and robustness.

The following verifies the error analysis of the algorithm in this paper and the original k-means fitting method for straight lines when the Hough transform threshold is set to 25, 30, 35, and 40, respectively. This article selects four noisy edge detection images for verification, as shown in Figure 19. In 19 (a), it can also be seen that for the same edge detection image, both algorithms show a decreasing trend in error as the Hough transform threshold continues to increase. But when , the error of the original k-means algorithm in fitting straight lines is significantly greater than the error of the algorithm in this paper. When , the errors of the two algorithms are close. Even under low threshold conditions of Hough transform, the algorithm proposed in this paper has a smaller error and obvious advantages, enabling it to achieve very small errors even on noisy edge detection images. And all four images show this pattern.

To verify the effectiveness of the method proposed in this article under different lighting conditions, some straight line detection results were taken as display, as shown in Figure 20. The images in group 20 (a) show the straight line detection results under outdoor natural lighting conditions. The shadow of the visible part has little impact on the detection results, with a slight deviation. It can detect the straight line of the suspension rod contour normally. Group 20 (b) images show the results of line detection under indoor lighting conditions, which can effectively complete the line detection task. Group 20 (c) images show the results of line detection under dark fill conditions, which can detect corresponding lines. Due to the dim light, there may be detection failures. 20 (d) sets of images show the results of line detection under bright fill light conditions, with sufficient fill light, which can effectively complete the task of line detection. The images in group 20 (e) show the results of line detection under strong supplementary lighting conditions. Under this condition, the light is strong, and compared to slightly darker supplementary lighting conditions, it significantly highlights the target object, making edge detection more accurate, and the overall detection effect is the best.

Identify the crane grab boom under different light source conditions, and test 70 images under each light source condition. The recognition accuracy results of the algorithm in this article are shown in Table 1. The average error range is within the interval [0,10], indicating successful recognition. The average error range is within the interval (10, ∞), indicating recognition failure. Under dark light conditions, the average detection error within the range of [0,2] accounts for 90.0%, and the recognition success rate is 92.9%. Under strong supplementary light conditions, the recognition success rate is the highest, with an average detection error of 97.1% within 0-2 pixels and a recognition accuracy of 98.6%.