Night Vehicle Recognition Method Based On Generative Adversarial Networks

Due to various environmental factors,nighttime traffic accidents cannot be fully understood based on physical evidence,personal evidence,and other evidence.Therefore,it is necessary to install traffic monitoring video equipment to collect and organize visual information,which is the key to identifying and detecting nighttime scenes.The nighttime lighting environment is not ideal,the visual effect of the collected vehicle videos is poor,there is a lack of sufficient lighting,and the scene is complex,thus increasing the difficulty of recognition.This thesis proposes a nighttime recognition method based on generative adversarial networks to address this issue.This method constructs models and algorithms based on multiple features such as vehicle image enhancement,image detection,and vehicle appearance,and collects a large amount of urban monitoring data as a dataset to verify the effectiveness of the algorithm presented in this article.(1)The problem of low accuracy in extracting vehicle image features at night.For this problem,the vehicle images in the source domain should be migrated to the target domain on the basis of the transfer learning model of the generative countermeasure network,so that the migrated vehicle images show strong image features in the target domain;Then,by extracting deep features from vehicle images processed through migration,Euclidean distance is used to calculate the similarity between the actual vehicle image and two different feature maps in the vehicle image database,clarifying the basic features of the vehicle as an important basis for detecting vehicles in nighttime environments.Through simulation experiments,the effectiveness of the proposed method in improving vehicle recognition performance is verified.This model can easily and accurately locate a large number of local areas in the perspective,reduce the cost of positioning investment,and also enhance the generalization ability of local learning.(2)Vehicles are affected by many interference factors in the nighttime environment,and there are significant differences in visual appearance features of the same vehicle image during the matching process from different perspectives,making it difficult to accurately determine whether the image is the same vehicle.Therefore,this paper proposes a nighttime vehicle recognition method based on multi perspective image generation,which generates eight corresponding vehicle images from all single perspective input images,Extract depth features from the original image and eight images from different perspectives,and then further fuse these features to generate enhanced features;Then compare and analyze all vehicle features from a normalized perspective to achieve vehicle recognition function.After the above operations,the problem of excessive matching error caused by excessive interference factors has been effectively addressed.Simulation experiments have verified that compared with ordinary vehicle feature matching methods,the accuracy and robustness of this method have been improved.