A traffic anomaly picture recognition method based on focus loss function

Abstract

The invention belongs to the technical field of image recognition and computer vision, and discloses a traffic abnormal picture recognition method based on a focus loss function. The method comprisesthe following steps: a convolution neural network based on a point loss function is constructed to establish a picture data set; the image data set is preprocessed and the convolutional neural networkis trained with the processed image data set. The trained convolution neural network is used to recognize the input picture, and the detection result of the traffic anomaly category is obtained. Theinvention uses focus loss function instead of original loss function to smoothly train the convolution neural network and improve recognition accuracy in view of the situation that the road abnormal and normal data set types are not balanced, which leads to difficult training of the convolution neural network; the invention uses focus loss function instead of the original loss function to smoothlytrain the convolution neural network. The identification method provided by the invention can be deployed in the monitoring system of each traffic section, and can automatically identify whether thetraffic anomaly occurs in the current road section, so as to provide timely early warning for the traffic department.

Description

technical field [0001] The invention belongs to the technical field of image recognition and computer vision, and more specifically relates to a method for recognizing traffic anomalies based on a focal loss function. Background technique [0002] There are many reasons for traffic accidents, including road snow, road damage, fire, traffic jams, heavy fog, landslides and other traffic abnormalities. If these reasons can be identified through monitoring, corresponding measures can be taken before traffic accidents occur. Reduce the occurrence of traffic accidents. [0003] At present, traffic anomalies are mainly identified by telephone and manual inspection of surveillance cameras, which is inefficient and requires a lot of human labor. One possible approach is to use computer vision techniques to write software that automatically detects whether images taken by surveillance cameras contain anomalies. Traditional computer vision technology needs to manually design features...

AI Technical Summary

Problems solved by technology

However, the ratio of traffic anomaly pictures obtained in actual application scenarios to normal pictures is very unbalanced, which will greatly affect the accuracy of the trained classifier, resulting in low accuracy in actual detection

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