Research on the Cascade Vehicle Detection Method Based on CNN
Autor: | Yuqi Sun, Jianjun Hu, Songsong Xiong |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Computer Networks and Communications
Local binary patterns Computer science multifeature fusion convolutional neural network lcsh:TK7800-8360 02 engineering and technology Convolutional neural network autonomous driving Robustness (computer science) Histogram 0502 economics and business 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering 050210 logistics & transportation business.industry Orientation (computer vision) Dimensionality reduction 05 social sciences lcsh:Electronics Pattern recognition Hardware and Architecture Control and Systems Engineering Cascade Feature (computer vision) Signal Processing vehicle detection cascade method 020201 artificial intelligence & image processing Artificial intelligence business |
Zdroj: | Electronics, Vol 10, Iss 481, p 481 (2021) Electronics Volume 10 Issue 4 |
ISSN: | 2079-9292 |
Popis: | This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradient histogram features from the front vehicle are extracted, then principal-component-analysis dimension reduction and serial-fusion processing are performed on the input image. Furthermore, a preliminary screening is conducted as the input of a support vector machine classifier based on the acquired fusion features, and the CNN model is employed to validate cascade detection of the filtered results. Finally, an integrated data set extracted from BDD, Udacity, and other data sets is utilized to test the method proposed. The recall rate is 98.69%, which is better than the traditional feature algorithm, and the recall rate of 97.32% in a complex driving environment indicates that the algorithm possesses good robustness. |
Databáze: | OpenAIRE |
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