Guangzhou University

Zhongkai University of Agriculture Engineering

Guangzhou Guangjian Construction Engineering Testing Center Co., Ltd.

Guangzhou Cheng'an Testing Ltd. of Highway & Bridge

Yongmin Yang

Jiyang Fu

Airong Liu

Bingcong Chen

Zhicheng Yang

Jianting Cheng

Jihua Mao

Jiaming Xu

Xiaosheng Wu

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.

Global and local binary pattern image crack segmentation method based on robot vision

Growing community of inventors

Guangzhou, China

Yongmin Yang