Pavement defect detection method and system based on deep learning

Abstract

The invention relates to a pavement defect detection method and system based on deep learning, wherein the method comprises the following steps: building an image segmentation model, taking a U-Net feature extraction network as a trunk by U-MDN, extracting multi-scale features obtained from different convolution stages, further extracting defect deep layer features by using U-MDM, efficiently fusing multi-scale features obtained in shallow layer features and the deep layer features to obtain a prediction result; and carrying out pavement defect quantitative evaluation: further calculating a pavement damage index based on the quantitative parameters to obtain a corresponding pavement damage grade. According to the method, the defect of single feature of common standard convolution extraction is overcome, redundant information can be removed, and the calculation amount can be reduced; and the pavement defects are detected in real time by using the improved deep neural network, the defects of a traditional manual pavement detection method are overcome, and the detection efficiency is improved.

Description

Technical field [0001] The present invention relates to the field of pavement defect detection, and more particularly to a depth study-based pavement defect detection method and system. Background technique [0002] With the rapid development of the transportation industry, my country has formed a road traffic network with a certain scale. A large number of surface defects generated during use marks the existing roads have entered a large area detection and maintenance phase. The more and more diversification and complexity of highway traffic networks have caused the performance requirements of the defect detection method to be higher and higher. Therefore, it is of great significance to find and more accurately and quickly achieve crack detection on road maintenance and driving safety. [0003] Pavement cracks are one of the most common diseases, which has a huge threat to road safety. It is possible to find that repair and prevent further deterioration has become an important t...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method and system for pavement defect detection based on deep learning. By setting U-MDM, the module adopts a four-scale up-down sampling structure, and combines the advantages of dilated convolution to overcome the commonly used standard convolution. The shortcoming of a single feature extracted by the product, and redundant information can be removed to reduce the amount of calculation

Images