Bridge crack detection method based on multi-resolution convolutional network

Abstract

The invention belongs to the technical field of image target detection, and particularly relates to a bridge crack detection method based on a multi-resolution convolutional network, which comprises the following steps: 1, constructing a bridge crack data set; 2, labeling and classifying the training samples; 3, building an improved HRBCS bridge crack semantic segmentation model based on the HRNetmodel; 4, training the semantic segmentation model built in the step 3; 5, testing the semantic segmentation model trained in the step 4; 6, performing actual detection. Compared with the prior art,the method has the advantages that the spatial accuracy is greatly improved, a high-resolution segmentation result can be obtained, segmentation of small cracks and complex cracks is greatly improved,different types of cracks can be detected and classified, and a high-quality bridge crack segmentation mask can be generated; therefore, a corresponding maintenance scheme is formulated according tothe damage degree of different types of crack evaluation to the bridge.

Description

technical field [0001] The invention belongs to the technical field of image target detection, and in particular relates to a bridge crack detection method based on a multi-resolution convolutional network. Background technique [0002] As an important carrier connecting two large-span locations, bridges play an important role in my country's road transportation. However, during the long-term sun, rain and load operation of the bridge, the internal stress generated will also be transmitted to some weak parts along the bridge structure, resulting in the occurrence and development of cracks on the surface of the structure at this position. The damage degree of cracks to bridge structures is also different. If the extension direction of surface cracks is perpendicular to the bearing surface of the structure, it is easy to cause unsafe accidents. [0003] According to engineering practice and theoretical analysis, most bridges in service work with cracks, and the potential dama...

AI Technical Summary

Problems solved by technology

However, for bridge crack detection work, the feature map obtained by the classic segmentation model has a low resolution, which loses the spatial structure information, resulting in low spatial accuracy of the final result.

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