Bridge Crack Identification and Monitoring Using IoT

Abstract

The appearance and progression of cracks in a concrete bridge will negatively impact how safely people can use bridge structures. This paper develops an image pre-processing scheme combining multiple adaptive filtering and contrast enhancement based on the image processing technology of concrete crack, which can improve the removal effect of background noise and obtain the characteristic in information of tiny cracks. This approach can better meet the crack detection requirement. Then, in order to retrieve the information about the crack edge and increase the positioning accuracy of the crack border, we developed a local adaptive technique of Otsu threshold segmentation and merged it with a modified Sobel operator for removing isolated noise spots. The target crack is also recognized, classed, and the feature data is calculated in accordance with the image feature of the bridge crack edge. The case analysis findings demonstrate that the detection algorithm's data processing accuracy can satisfy the actual engineering criteria for concrete bridge crack detection by processing data to a precision of 0.02mm. Key Word: Multiple adaptive filtering, Contrast enhancement, Background noise, Local adaptive filtering, Otsu threshold segmentation.