EIoU: An Improved Vehicle Detection Algorithm Based on VehicleNet Neural Network

Autor:	Zuomin Yang, Jianguang Li, Xianlun Wang
Rok vydání:	2021
Předmět:	History Artificial neural network Computer science business.industry Vehicle detection Pattern recognition Artificial intelligence business Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1924:012001
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1924/1/012001
Popis:	The paper’s primary purpose is to optimize the performance (speed/accuracy) of vehicle detection. The vehicle dataset Vehicle2020 used in this paper is divided into ten different vehicle classes. Intersection over Union (IoU) is usually used as a standard to evaluate the accuracy of vehicle detection in a specific dataset. However, IoU as a performance of the object detection algorithm is still shortcomings. IoU is further improved and called a new algorithm EIoU. Finally, the neural network structure was redesigned, which was called VehicleNet. The experimental results show that EIoU as a performance evaluation algorithm used the vehicle detection framework can improve the performance of vehicle detection. Using the algorithm of this paper shows the performance superiority of vehicle detection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::03eccbc1d09eedfa916197f397f8fc59 https://doi.org/10.1088/1742-6596/1924/1/012001 Zobrazit plný text záznamu