A complicated background Bridge crack image crack detection method
A complex background and detection method technology, applied in the field of computer vision, can solve problems such as unsatisfactory results, complex background textures, and small image areas, and achieve good generalization ability, reduced training time, and strong recognition effects.
Active Publication Date: 2020-11-10
SHAANXI NORMAL UNIV
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If the existing semantic segmentation model is directly applied to the detection of bridge pavement cracks, due to the linear topological structure of the cracks, which occupy a small area of the entire image, and the background texture is complex and there are many obstacles, satisfactory results cannot be achieved.
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[0040] The present invention will be described in further detail below in conjunction with specific examples, but the embodiments of the present invention are not limited thereto.
[0041] Such as Figure 17 As shown, this embodiment provides a method for crack detection of bridge crack images under complex backgrounds, which includes the following steps: Step 1, performing geometric transformation, spatial filtering, and linear transformation on the collected original bridge crack images, and then generating sub-models and discriminating The sub-model performs data set amplification; the generated sub-model sequentially includes a fully connected layer, a dimension conversion layer, a first transposed convolution layer, a second transposed convolution layer, a third transposed convolution layer, and a fourth transposed convolution Convolution layer and fifth transposed convolutional layer; first transposed convolutional layer, second transposed convolutional layer, third tran...
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The invention relates to a crack detection method for bridge crack images under complex backgrounds. First, according to the principle of deep convolution generation confrontation network, a bridge pavement crack image generation model is proposed and used to amplify the data set; A bridge pavement crack image segmentation model for semantic segmentation; finally, the cracks in the crack image are extracted using the bridge pavement crack image segmentation model. The expansion of the data set effectively alleviated the underfitting phenomenon caused by insufficient training data, and the precision rate and recall rate increased by 79.4% and 74.7% respectively. Compared with the existing semantic segmentation algorithm, the algorithm reduces the number of parameters, reduces the training time, and improves the precision rate, recall rate, and F1 score to more than 92%. Compared with existing bridge pavement crack detection and segmentation algorithms, this algorithm is more suitable for bridge pavement crack detection and segmentation under complex background, and has stronger recognition effect and better generalization ability.
Description
technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for detecting cracks in bridge crack images under complex backgrounds. Background technique [0002] Transportation is the basic need and prerequisite for economic development, the survival foundation and civilization symbol of modern society, and the infrastructure and important link of industrial development. It is related to the development of the national economy and carries the lifeline of social progress. According to the "Statistical Bulletin on National Economic and Social Development in 2017", in 2017, 6,796 kilometers of highways were newly rebuilt in my country, and 2,182 kilometers of new high-speed railways were put into operation. Influence has increased significantly. In the construction of modern transportation, bridges account for 50% of the lines in the high-speed railways that have been constructed, and the proportion of bridges in ...
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IPC IPC(8): G06T7/00G06T7/10G06N3/04
CPCG06T7/0004G06T7/10G06T2207/20081G06T2207/10004G06N3/045
Inventor 李良福孙瑞赟
Owner SHAANXI NORMAL UNIV