A Method for Detection and Classification of Pavement Cracks Based on 3D Laser Sensor and Bounding Box
A three-dimensional laser, classification method technology, applied in the direction of optical testing flaws/defects, instruments, calculations, etc., can solve the problems of not considering the impact, not supporting multiple crack distinction, incompatibility, etc., to achieve cost and workload reduction, efficient automatic Collect and improve work efficiency
Active Publication Date: 2021-11-09
FUJIAN AGRI & FORESTRY UNIV
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Problems solved by technology
The detection based on ANN technology has satisfactory results for various types of cracks; however, there are still two limitations: 1) It is not suitable for large-scale road network surveys due to the difficulty in establishing a training library
2) It cannot distinguish between multiple cracks in the same image
[0007] Existing fracture classification techniques have several limitations: (1) do not consider fracture characteristics such as: length, width, perimeter, area
(2) Regardless of the complexity of cracks
(3) Longitudinal and transverse cracks cannot be easily separated
(4) Does not support the distinction of multiple cracks in the same image (5) Does not consider the influence of crack position on cracks
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[0052] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
[0053] figure 1 It is the vehicle-mounted three-dimensional laser sensor of this embodiment. It can be used to collect two-dimensional or three-dimensional data of the whole lane at a speed of 100km / h on the expressway, and obtain a three-dimensional image of the road surface with an accuracy of 1mm / pixel, which makes up for the traditional two-dimensional image quality that is easily affected by lane markings, shadows and Defects such as oil pollution can significantly improve the quality of road surface images; its coverage length can reach 4m wide, and it can collect road surface image data of the entire lane at one time. In this embodiment, three-dimensional images are used as basic elements for detection and classification. Each 3D image has a size of 4096 mm and a width of 2048 mm. To detect and identify cracks, histogram equalization and adapt...
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The invention relates to a road surface crack detection and classification method based on a three-dimensional laser sensor and a bounding box. Two independent laser sensors and three-dimensional laser scanning imaging technology are installed on the vehicle to collect two-dimensional or three-dimensional road image data of the entire lane. After collecting relevant data, the multi‑seed fusion algorithm is used to identify cracks. Then image processing techniques such as dilation and erosion are introduced, combined with the location references of wheel paths and lane markings to generate the final bounding box. Finally, crack classification and severity rating evaluation based on the bounding box. The invention can identify and classify the cracks at the initial stage of appearance, greatly reduces the maintenance cost, and has the advantages of fastness, high efficiency and high accuracy.
Description
technical field [0001] The invention relates to the technical field of automatic road detection, in particular to a road surface crack detection and classification method based on a three-dimensional laser sensor and a bounding box. Background technique [0002] At present, a large amount of research at home and abroad has been devoted to the development of available solutions for fully automatic fracture classification, but the field application of the results is still limited because of the many defects in the existing processing technology. [0003] For automatic classification techniques, they can be divided into three categories: wave transform-based computation, artificial neural network-based techniques, and statistical feature-based techniques. [0004] The pavement image can be decomposed into different frequency sub-bands by wavelet transform, in which the disease is transformed into high-amplitude wavelet coefficients, and the background is transformed into wavele...
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IPC IPC(8): G01N21/88G06K9/00G06K9/34
Inventor 李林罗文婷
Owner FUJIAN AGRI & FORESTRY UNIV