Research on image recognition based on the yolov3 model in an extreme environment

Research Article
Open access

Research on image recognition based on the yolov3 model in an extreme environment

Haiyang Jiang 1*
  • 1 Xidian University    
  • *corresponding author 673542471@qq.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/20/20231097
ACE Vol.20
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-031-8
ISBN (Online): 978-1-83558-032-5

Abstract

In recent years, the human demand for the exploration and application of extreme environment is increasing. However, the light, climate, object motion and other factors in extreme environments have great uncertainty and complexity, which makes the image recognition technology face great challenges. This study aims to investigate the image recognition techniques based on the yolov3 (You Only Look Once Version 3) model in extreme environments. For the problem of image recognition in extreme environments, this study compared the identification gap between the initial data set in the yolov3 model, and optimized the yolov3 model (The main way is to prune and quantify, and fine-tune the algorithm) to improve its accuracy and stability in extreme environments. In this study, the feasibility of yolov3 model for image recognition in extreme environment was verified by comparing the performance of yolov3 model before and after optimization. The experimental results show that the image recognition technology based on yolov3 model proposed in this study has high accuracy and stability in extreme environments.

Keywords:

extreme environment, image recognition, yolov3 model, deep learning, convolution neural network

Jiang,H. (2023). Research on image recognition based on the yolov3 model in an extreme environment. Applied and Computational Engineering,20,195-200.
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References

[1]. Redmon J , Farhadi A .YOLOv3: An Incremental Improvement[J].arXiv e-prints, 2018.

[2]. Zhang, H., Cao, Z., Liu, Y., & Zhang, L.(2018).Learning deep cnn denoiser prior for image restoration.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.2808-2817).

[3]. Cai R .Research Progress in Image Denoising Algorithms Based on Deep Learning[J].Journal of Physics Conference Series, 2019, 1345:042055.

[4]. Pang T , Zheng H , Quan Y , et al.Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising[C]// Computer Vision and Pattern Recognition.IEEE, 2021.

[5]. CDSN. (2018) Yolo v3 of yolo series. https://docs.voxel51.com/integrations/coco.html/

[6]. Liu J ,Liu X ,Shao Z , et al.Research on blind restoration of noisy blurred image based on deep learning[C]// ICASIT 2020: 2020 International Conference on Aviation Safety and Information Technology.2020.

[7]. Gai Shan, Bao Hongyun. Deep learning-based high-noise image denoising algorithm [J]. Journal of Automation, 2020,46 (12): 9.

[8]. Li Chuanpeng. Image denoising study based on deep convolutional neural networks [J]. Computer Engineering, 2017, (3): 253-260.

[9]. Li Songshun, Zhou Junwei, Du Zhenhua, et al. A model optimization algorithm based on the target detection YOLOv3 of deep learning:, CN112001477A [P].2020.

Cite this article

Jiang,H. (2023). Research on image recognition based on the yolov3 model in an extreme environment. Applied and Computational Engineering,20,195-200.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-031-8(Print) / 978-1-83558-032-5(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.20
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Redmon J , Farhadi A .YOLOv3: An Incremental Improvement[J].arXiv e-prints, 2018.

[2]. Zhang, H., Cao, Z., Liu, Y., & Zhang, L.(2018).Learning deep cnn denoiser prior for image restoration.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp.2808-2817).

[3]. Cai R .Research Progress in Image Denoising Algorithms Based on Deep Learning[J].Journal of Physics Conference Series, 2019, 1345:042055.

[4]. Pang T , Zheng H , Quan Y , et al.Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising[C]// Computer Vision and Pattern Recognition.IEEE, 2021.

[5]. CDSN. (2018) Yolo v3 of yolo series. https://docs.voxel51.com/integrations/coco.html/

[6]. Liu J ,Liu X ,Shao Z , et al.Research on blind restoration of noisy blurred image based on deep learning[C]// ICASIT 2020: 2020 International Conference on Aviation Safety and Information Technology.2020.

[7]. Gai Shan, Bao Hongyun. Deep learning-based high-noise image denoising algorithm [J]. Journal of Automation, 2020,46 (12): 9.

[8]. Li Chuanpeng. Image denoising study based on deep convolutional neural networks [J]. Computer Engineering, 2017, (3): 253-260.

[9]. Li Songshun, Zhou Junwei, Du Zhenhua, et al. A model optimization algorithm based on the target detection YOLOv3 of deep learning:, CN112001477A [P].2020.

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