Implementation of Small Sample Citrus Fruit Classification Detection Based on Improved ResNet18 Residual Network

Authors

  • Hai Li
  • Hua Qing
  • Shihui Wu
  • Haiqing Bai
  • Bo Lin

DOI:

https://doi.org/10.54097/tv2wxx35

Keywords:

Resnet; classification detection.

Abstract

Citrus is an important means of production, and traditional citrus classification using manual labor is time-consuming, labor-intensive, and costly. This article proposes a new residual block structure based on the introduction of pre activated residual block mode to build the ResNet18 network model, establish a small sample database of 4000 citrus fruits in four categories and use this network model for classification detection. The average accuracy of the final classification detection of ResNet18 after optimization reached 93.764%, the training time of the model was reduced by 41.47%, and the improved model showed improvements in both training speed and accuracy.

Downloads

Download data is not yet available.

References

[1] Zhang Chenyu, Nie Shuping, Zhang Hongzhen, etc Design of an Automated Citrus Picking Robot [J] Agricultural Machinery Use and Maintenance, 2023, (09): 10-15

[2] Xiong Longye Research on Classification, Recognition, and Localization Methods of Mature Citrus Fruits in Natural Scenes [D] Chongqing University of Technology, 2020

[3] Pan Hong Research on Multimodal Medical Image Recognition of Thyroid Nodules Based on Deep Learning [D] Zhejiang University of Traditional Chinese Medicine, 2022

[4] Xu Pingting, Zheng Jiaqi A Strong Dog Detection and Reminder System Based on Deep Learning Algorithms [J] Software, 2023

[5] Yang Hang Identification algorithm and research of ID card number [D] Chongqing University of Posts and Telecommunications, 2017

[6] Xiang Chao Research on Human Posture Analysis of Video Images in Unmanned Aerial Vehicle Systems [D] Nanjing University of Aeronautics and Astronautics, 2020

[7] Xia Minggui, Tian Ruijun, Jiang Huiyu, etc Research on Clothing Image Style Recognition Based on Improved ResNet Network and Transfer Learning [J] Journal of Textile Engineering, 2024, 2 (01): 12-20

[8] Fan Liming Research on the Classification Method of Cervical Cell Pathology Images Based on Deep Learning [D] Xiangtan University, 2021

[9] A. Faraji, M. Noohi, S. A. Sadrossadat, A. Mirvakili, W. Na and F. Feng, "Batch-Normalized Deep Recurrent Neural Network for High-Speed Nonlinear Circuit Macromodeling," in IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 11, pp. 4857-4868, Nov. 2022, doi: 10.1109/TMTT.2022.3200071

Downloads

Published

29-11-2024

Issue

Section

Articles

How to Cite

Li, H., Qing, H., Wu, S., Bai, H., & Lin, B. (2024). Implementation of Small Sample Citrus Fruit Classification Detection Based on Improved ResNet18 Residual Network. Academic Journal of Science and Technology, 13(2), 201-206. https://doi.org/10.54097/tv2wxx35