research-article

Algae Image Classification Algorithm Based on the Improved MobileNetV2

Authors:

Zhisong QinAuthors Info & Claims

CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software Engineering

Pages 75 - 79

https://doi.org/10.1145/3569966.3569988

Published: 20 December 2022 Publication History

Abstract

To address the problems of large number of parameters, poor real-time performance and low classification accuracy of existing algae image classification models, this paper proposes a lightweight model based on MobileNetV2. By using the GELU activation function instead of the RELU activation function, the generalization ability of the model and classification accuracy are improved; In order to establish the dependency between channel information and location information, a lightweight coordinate attention mechanism is embedded in the model. The experimental results show that the model can efficiently identify algae categories, and the overall recognition accuracy of the model reaches 97.0% on the algae image dataset after convergence. Moreover, the number of model parameters is only 10.68M, which has certain practical application value.

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Cited By

Puspasari BChang IPramono AYulianti T(2024)EfficientNet-Based Sugarcane Disease Classification with Dual-Convolution Spatial Attention CBAM (EfficientNet-DCCBAM)2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)10.1109/COMNETSAT63286.2024.10862924(1-6)Online publication date: 28-Nov-2024
https://doi.org/10.1109/COMNETSAT63286.2024.10862924

Index Terms

Algae Image Classification Algorithm Based on the Improved MobileNetV2
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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cover image ACM Other conferences

CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software Engineering

October 2022

753 pages

ISBN:9781450397780

DOI:10.1145/3569966

Copyright © 2022 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 December 2022

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Innovation Project of GUET Graduate Education
Key Laboratory of Environmental Opatics and Technology, CAS
Science and Technology Major Project of Guangxi Zhuang Autonomous Region Government

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CSSE 2022

CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering

October 21 - 23, 2022

Guilin, China

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Overall Acceptance Rate 33 of 74 submissions, 45%

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Cited By

Puspasari BChang IPramono AYulianti T(2024)EfficientNet-Based Sugarcane Disease Classification with Dual-Convolution Spatial Attention CBAM (EfficientNet-DCCBAM)2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)10.1109/COMNETSAT63286.2024.10862924(1-6)Online publication date: 28-Nov-2024
https://doi.org/10.1109/COMNETSAT63286.2024.10862924

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