Research On Visual Detection And Intelligent Recognition Of Surface Defects Of Bridge Cable Sheath

Cables are an important component of cable-stayed bridges and are critical to bridge safety.The outer side of the bridge cable usually has a high-polyethylene sheath,and the sheath will have defects such as cracks and holes under the action of light,wind and water vapor.These deficiencies pose a potential threat to bridge safety Regular inspection and maintenance of the sheath ensures safe use of the cable.In this paper,the machine vision-based defect detection method is studied,and a relatively complete detection scheme is formed.It is expected to replace the manual inspection of sheath defects in the futureThe main work of this paper is:1)A surface defect visual inspection scheme is designed,in which the image acquisition module includes a CMOS industrial camera,an 8mm fixed focus lens,and a ring LED industrial light source for collecting the surface image of the sheath.The online algorithm mainly completes image graying,image denoising,image segmentation,defect discrimination and save defective images.The offline algorithm completes image stitching,feature extraction,feature selection and image classification tasks2)Image preprocessing and image segmentation algorithms are studied.The acquired image is grayed out using the YUV color space,and the adaptive median filtering algorithm is used for denoising to preserve the original image information The image segmentation of the defect is completed by the method of sobel operator combined with threshold segmentation and morphological processing.Finally,the area and the length of the long axis are used to determine whether it is a defect image,and the long axis angle is calculated by PCA algorithm3)For the case where defects may exist at the junction of adjacent cameras,the image stitching algorithm is studied.In the feature point selection stage,the feature-stable and information-rich SIFT algorithm is used,and the FAST feature point extraction algorithm is used to replace the feature point selection part of SIFT,which greatly improves the computational efficiency.After screening to remove the pair of mismatched points,the RANSAC method is used to calculate the optimal transformation matrix.Finally,the two images are merged by using the gray weighted average method to obtain the final stitching result4)In order to obtain more effective information,the detected defect images are classified,The classification of defect and related algorithms are studied.Sixteen candidate features are extracted from the acquired images.Based on this,feature selection algorithms are used to select 10 features with good separability as classification features.This process can reduce the information dimension and remove redundant information.Thereby improving the efficiency and accuracy of the classification algorithm.Classification is done using the SVM classifier,and the parameters c and o-that affect the performance of the classifier are optimized using the wolf group algorithm,and the final classifier achieves 97.5%classification accuracy.In this paper,the key algorithms involved in the detection of bridge cable surface defects are studied and experiment.The results show that the algorithm in this paper can effectively remove the original interference information in the image and keep it intact.The defect image and the extraction of effective defect features lay the foundation for classification.Finally,the optimization of the intelligent algorithm improves the accuracy of the classification of defect.In addition,the algorithm in this paper has good real-time performance and can meet the needs of actual detection.