Image-based Adaptive Constant False Alarm Detection Method for Pavement Crack Disease

Abstract

The image-based pavement disease crack identification method of the present invention first extracts the pixels of the image by column or row one by one to form a one-dimensional signal, regards the extracted data as a radar pulse echo signal, and uses the constant false alarm The detection technology determines whether there are crack features in the data, and retains the signal waveform data with crack information. After the row or column data of all images are processed, an image containing only cracks is obtained. Finally, the classification method is used to classify different cracks. discriminate. Beneficial effects: the method is simple and effective, and the direct benefit is the reduction of computational complexity. In terms of hardware implementation, lower modules or devices can be used to complete all detection tasks, thereby reducing the manufacturing cost of manufacturers.

Description

technical field [0001] The invention relates to the field of image processing and analysis, in particular to an image-based adaptive constant false alarm detection method for pavement crack disease. Background technique [0002] The rapid development of my country's highway construction has attracted the attention of the world. The general highway has reached the current 4 million kilometers from the tens of thousands of kilometers in the early days of the founding of the People's Republic of China, and the expressway has reached the current 78,000 kilometers since the opening of the Shanghai-Jiajia Expressway in 1988, ranking No. 1 in the world. Two, second only to the United States. According to the "National Highway Network Planning" issued by the Ministry of Communications, the combination of radial lines and vertical and horizontal networks is adopted: by 2020, the total scale of my country's highway network will reach 4.2 million kilometers, and the expressway will reac...

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Problems solved by technology

The problems of the above methods are still many links, complex processing, high calculation cost, poor adaptability to the environment, over-reliance on disease edge detection, only effective for serious diseases, detection stability and there are still problems such as difficulty in guaranteeing.

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