4 CONCLUSION

1) To solve the problem of difficult detection of the corner points of the crane’s grab boom during the alignment process between the boom and the segmental beam body lifting hole. This article proposes a method for constructing grayscale difference maps, which presents a bimodal feature between the region to be segmented and the background structure, and it more suitable for binary processing using the Otsu algorithm. Accurately extract the edge image of the crane grabbing boom under sufficient lighting conditions and without obstructions. The image preprocessing method in this article solves the problem of over segmentation and under segmentation that other image segmentation methods are prone to, and can meet the segmentation requirements under different lighting conditions. The method proposed in this article has the advantage of fast computation speed, and compared to semantic segmentation, it does not require a large amount of time to produce datasets to train neural networks.
2) The threshold setting of Hough transform requires artificial selection. Due to the changing weather, lighting conditions, construction environment and other external conditions, it is necessary to ensure the reliability of application and engineering application. Combining the Hough transform with k-means clustering, the Hough line detection threshold is set low, simplifying lines with the same features into one, providing a new approach for corner detection.
3) The method proposed in this article for determining the optimal adaptive threshold by counting the number of votes removes the error lines in the low threshold of the Hough transform and ensures that there is no missed detection; Replace the original cluster centroid with an improved clustering centroid calculation method as the basis for line fitting, Improved the accuracy of line fitting under the same lighting conditions and its robustness under different uniform lighting conditions. The algorithm in this article has the best detection performance under strong complementary light conditions. The average detection error within 0-2 pixels accounts for 97.1% and a recognition accuracy of 98.6%. The recognition success rate under different lighting conditions is higher than 92.9%. This method is significantly superior to traditional linear detection methods and meets the needs of automatic gripping of the boom.