The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 14, 2023
Filed:
Apr. 21, 2022
Guangzhou University, Guangzhou, CN;
Zhongkai University of Agriculture Engineering, Guangzhou, CN;
Guangzhou Guangjian Construction Engineering Testing Center Co., Ltd., Guangzhou, CN;
Guangzhou Cheng'an Testing Ltd. of Highway & Bridge, Guangzhou, CN;
Jiyang Fu, Guangzhou, CN;
Airong Liu, Guangzhou, CN;
Zhicheng Yang, Guangzhou, CN;
Jihua Mao, Guangzhou, CN;
Bingcong Chen, Guangzhou, CN;
Jiaming Xu, Guangzhou, CN;
Yongmin Yang, Guangzhou, CN;
Xiaosheng Wu, Guangzhou, CN;
Jianting Cheng, Guangzhou, CN;
Guangzhou University, Guangzhou, CN;
Zhongkai University of Agriculture Engineering, Guangzhou, CN;
Guangzhou Guangjian Construction Engineering Testing Center Co., Ltd., Guangzhou, CN;
GuangZhou Cheng'an Testing LTD. of Highway & Bridge, Guangzhou, CN;
Abstract
A global and local binary pattern image crack segmentation method based on robot vision comprises the following steps: enhancing a contrast of an acquired original image to obtain an enhanced map; using an improved local binary pattern detection algorithm to process the enhanced map and construct a saliency map; using the enhanced map and the saliency map to segment cracks and obtaining a global and local binary pattern automatic crack segmentation method; and evaluating performance of the obtained global and local binary pattern automatic crack segmentation method. The present application uses logarithmic transformation to enhance the contrast of a crack image, so that information of dark parts of the cracks is richer. Texture features of a rotation invariant local binary pattern are improved. Global information of four directions is integrated, and the law of universal gravitation and gray and roundness features are introduced to correct crack segmentation results, thereby improving segmentation accuracy. Crack regions can be segmented in the background of uneven illumination and complex textures. The method has good robustness and meets requirements of online detection.