As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
In order to alleviate the increasing number of vehicles every year and make traffic congestion become more and more serious; an intelligent networked vehicle safety detection mechanism based on deep learning is proposed. Intelligent traffic management can effectively alleviate traffic congestion, and vehicle detection is an important part of intelligent transportation implementation. Traditional vehicle detection methods are inefficient and also rely on manual operation, and have poor robustness. Vehicle detection based on deep learning can solve the above problems well. The results show that with the training, box_loss, obj_loss and cls_loss all approach zero. The model can accurately detect the vehicles on the way, and mark the vehicles with boxes, with a high confidence level between 0.69 and 0.88.
Conclusion:
The Yolov5 algorithm and UA-DETRAC dataset are used to realize the intelligent detection of vehicles. The trained model is used to detect vehicles in the picture. The model accuracy is high and the detection results are good.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.