IEICE TRANSACTIONS on Information
ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network
Summary :
Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.4 pp.488-494
- Publication Date
- 2023/04/01
- Publicized
- 2022/11/22
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022IIP0009
- Type of Manuscript
- Special Section PAPER (Special Section on Intelligent Information Processing to Solve Social Issues)
- Category
Authors
Fuxiang LIU
Beijing Institute of Technology
Chen ZANG
Beijing Institute of Technology
Lei LI
Science and Technology on Avionics Integration Laboratory
Chunfeng XU
Beijing Institute of Technology
Jingmin LUO
CHINAROCKERT CO., LTD.
Keyword
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Fuxiang LIU, Chen ZANG, Lei LI, Chunfeng XU, Jingmin LUO, "ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 4, pp. 488-494, April 2023, doi: 10.1587/transinf.2022IIP0009.
Abstract: Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022IIP0009/_p
Copy
@ARTICLE{e106-d_4_488,
author={Fuxiang LIU, Chen ZANG, Lei LI, Chunfeng XU, Jingmin LUO, },
journal={IEICE TRANSACTIONS on Information},
title={ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network},
year={2023},
volume={E106-D},
number={4},
pages={488-494},
abstract={Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.},
keywords={},
doi={10.1587/transinf.2022IIP0009},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - ConvNeXt-Haze: A Fog Image Classification Algorithm for Small and Imbalanced Sample Dataset Based on Convolutional Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 488
EP - 494
AU - Fuxiang LIU
AU - Chen ZANG
AU - Lei LI
AU - Chunfeng XU
AU - Jingmin LUO
PY - 2023
DO - 10.1587/transinf.2022IIP0009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2023
AB - Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.
ER -
English
