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ID 115186
Author
Dongyang, Lyu Tokushima University
Keywords
Vehicle detection
vehicle classification
Yolov2-tiny
AlexNet
spatial pyramid pooling
CNN
SVM
Content Type
Journal Article
Description
In this paper, we propose vehicle detection and classification in a real road environment using a modified and improved AlexNet. Among the various challenges faced, the problem of poor robustness in extracting vehicle candidate regions through a single feature is solved using the YOLO deep learning series algorithm used to propose potential regions and to further improve the speed of detection. For this, the lightweight network Yolov2-tiny is chosen as the location network. In the training process, anchor box clustering is performed based on the ground truth of the training set, which improves its performance on the specific dataset. The low classification accuracy problem after template-based feature extraction is solved using the optimal feature description extracted through convolution neural network learning. Moreover, based on AlexNet, through adjusting parameters, an improved algorithm was proposed whose model size is smaller and classification speed is faster than the original AlexNet. Spatial Pyramid Pooling (SPP) is added to the vehicle classification network which solves the problem of low accuracy due to image distortion caused by image resizing. By combining CNN with SVM and normalizing features in SVM, the generalization ability of the model was improved. Experiments show that our method has a better performance in vehicle detection and type classification.
Journal Title
International Journal of Machine Learning and Computing
ISSN
20103700
Volume
11
Issue
4
Start Page
304
End Page
310
Published Date
2021-07
Rights
© 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)(https://creativecommons.org/licenses/by/4.0/).
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language
eng
TextVersion
Publisher
departments
Science and Technology