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Extreme Image Classification Algorithm Based on Multicore Dense Connection Network

Author

Listed:
  • Daolei Wang
  • Tianyu Zhang
  • Rui Zhu
  • Mingshan Li
  • Jiajun Sun
  • Bekir Sahin

Abstract

Extreme images refer to low-quality images taken under extreme environmental conditions such as haze, heavy rain, strong light, and shaking, which will lead to the failure of the visual system to effectively recognize the target. Most of the existing extreme image restoration algorithms only handle the restoration work of a certain type of image; how to effectively recognize all kinds of extreme images is still a challenge. Therefore, this paper proposes a classification and restoration algorithm for extreme images. Due to the large differences in the features on extreme images, it is difficult for the existing models such as DenseNet to effectively extract depth features. In order to solve the classification problem in the algorithm, we propose a Multicore Dense Connection Network (MDCNet). MDCNet is composed of dense part, attention part, and classification part. Dense Part uses two dense blocks with different convolution kernel sizes to extract features of different sizes; attention part uses channel attention mechanism and spatial attention mechanism to amplify the effective information in the feature map; classification part is mainly composed of two convolutional layers and two fully connected layers to extract and classify feature images. Experiments have shown that the recall of MDCNet can reach 92.75% on extreme image dataset. At the same time, the mAP value of target detection can be improved by about 16% after the image is processed by the classification and recovery algorithm.

Suggested Citation

  • Daolei Wang & Tianyu Zhang & Rui Zhu & Mingshan Li & Jiajun Sun & Bekir Sahin, 2021. "Extreme Image Classification Algorithm Based on Multicore Dense Connection Network," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, June.
  • Handle: RePEc:hin:jnlmpe:6616325
    DOI: 10.1155/2021/6616325
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    Cited by:

    1. Jianfeng Xu & Chenglei Wu & Jilin Xu & Lan Liu & Yuanjian Zhang, 2023. "Stream Convolution for Attribute Reduction of Concept Lattices," Mathematics, MDPI, vol. 11(17), pages 1-19, August.

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