Bridge vehicle detection method based on aerial images of unmanned aerial vehicle

Abstract

The invention discloses a bridge vehicle detection method based on aerial images of an unmanned aerial vehicle, and the method comprises the steps: carrying out the preprocessing of the aerial image of the unmanned aerial vehicle, and inhibiting the jitter and noise interference caused by sea wind; using a K-means clustering method for extracting a bridge part image; carrying out defogging processing on the image by using a dark channel graph and a fog graph model; solving a graying weight ratio by using a linear regression model; carrying out graying processing on the image; reducing the datasize of the bridge part image; carrying out contrast enhancement on the image by using a local contrast enhancement method; detecting a vehicle by using an Otsu threshold segmentation technology forhighlighting vehicle characteristics in a bridge part image, and finally, classifying the vehicles by using the vehicle type classifier based on the convolutional neural network, and calculating the length, width and height information of the vehicles by designing an empirical formula, so that the sea-crossing bridge vehicle information obtained by the method is accurate and reliable, and can be used for evaluating the bridge health condition of the sea-crossing bridge, predicting the bridge maintenance period, predicting the traffic guidance of a traffic department and the like.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a bridge vehicle detection method based on aerial images of unmanned aerial vehicles. Background technique [0002] The vehicle information in the aerial images of the cross-sea bridge is an important part of the construction of the intelligent transportation system, which is helpful to analyze the current traffic conditions of the cross-sea bridge and predict the maintenance period of the bridge. Vehicle features can be used for vehicle tracking, model analysis, license plate recognition, etc. At present, the commonly used vehicle detection method is based on the first-order or second-order derivative edge detection method. The disadvantage of this method is that the detection is greatly affected by the image quality, and high-quality images are required to obtain good vehicle detection results. . [0003] The images of the cross-sea bridge taken by drones are interfered by wat...

AI Technical Summary

Problems solved by technology

This method is only applicable to the situation where the shooting background is single and there are few sky areas or white areas in the image

Due to fog, reflection and other reasons in the aerial image of the cross-sea bridge, using this method directly to remove the fog will cause a large error

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