Highway pavement disease detection method based on improved YOLOv4

Abstract

The invention discloses a highway pavement disease detection method based on improved YOLOv4, which can quickly detect various types of pavement diseases. A camera is triggered to shoot through a mileage sensor, and a highway pavement image is obtained in a vehicle driving process; and the pavement disease in the pavement image is detected by using the pavement disease detection model based on the improved YOLOv4. In the CSPDarknet-53 backbone network of YOLOv4, the depth separable convolution is adopted to replace the common convolution, so that the calculation amount of network parameters is reduced. In a loss function calculation stage, a loss function of YOLOv4 is improved based on a Focal loss function, and the problem of low detection precision caused by imbalance of positive and negative samples in a network training process is solved; and mileage position data is added to a detection result to form pavement disease comprehensive information. The method provided by the invention can realize automatic and real-time detection of highway pavement diseases, greatly reduces the cost, improves the detection efficiency, and has a better detection effect compared with a traditional target detection technology.

Description

technical field [0001] The invention belongs to the field of road surface disease detection, and in particular relates to a highway pavement disease detection method based on improved YOLOv4 deep learning. Background technique [0002] As an important infrastructure, highways have been built in large quantities to provide efficient, convenient and safe transportation methods for economic development. However, during the use of roads, due to the influence of various natural factors such as large-scale vehicles, serious overloading, repeated rolling of wheels, ice, rain and snow, various diseases will appear during the use of roads. If these diseases are not treated in time at the initial stage, it will not only affect the appearance of the road, reduce the comfort of driving, reduce the load-bearing capacity and service life of the road, but also induce more serious damage, and at least cause greater investment in maintenance funds. Seriously cause traffic accidents, cause s...

AI Technical Summary

Problems solved by technology

[0007] Based on this, it is necessary to provide an improved YOLOv4 based on the current situation that the traditional road pavement disease detection is time-consuming and laborious, and the accuracy of the results is poor, and the road pavement disease detection method based on deep learning can well realize the disease feature learning and feature classification. Detection method of highway pavement disease

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