YOLO, Faster R-CNN, and SSD for cloud detection

Research Article
Open access

YOLO, Faster R-CNN, and SSD for cloud detection

Fenglin Yu 1*
  • 1 Wuhan University    
  • *corresponding author morest@whu.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/37/20230514
ACE Vol.37
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-299-2
ISBN (Online): 978-1-83558-300-5

Abstract

Object detection is an essential problem in computer vision. Many models perform well in different kinds of object detection problems. However, there needs to be more research on object detection for similar objects in traditional research fields. To study the performance of standard target detection models in similar target detection, this paper uses the cloud detection problem that requires higher accuracy than detection speed as an example. This paper trained and tested three models, YOLO, Faster R-CNN, and SSD, with our data set and obtained excellent detection results. On this basis, this paper puts forward some reasons that may cause the detection accuracy not to be high and puts forward the corresponding optimisation methods for these reasons, hoping to provide some ideas and help to solve this kind of problem.

Keywords:

Cloud Detection, Computer Vision, YOLO, Faster R-CNN, SSD

Yu,F. (2024). YOLO, Faster R-CNN, and SSD for cloud detection. Applied and Computational Engineering,37,239-247.
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References

[1]. Amit, Y.,, Felzenszwalb, P., and Girshick, R., 2020. Object detection. Computer Vision, pp.1–9.

[2]. Zou, Z.,, Chen, K.,, Shi, Z.,, Guo, Y., and Ye, J., 2023. Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3), pp.257–276.

[3]. Lee, J.,, Weger, R.C.,, Sengupta, S.K., and Welch, R.M., 1990. A neural network approach to cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 28(5), pp.846–855.

[4]. Baghel, V.S.,, Srivastava, A.M.,, Prakash, S., and Singh, S., 2020. Minutiae points extraction using faster R-CNN. Advances in Intelligent Systems and Computing, pp.3–10.

[5]. Li, R., and Wu, Y., 2022. Improved Yolo V5 wheat ear detection algorithm based on attention mechanism. Electronics, 11(11), p.1673.

[6]. Amari, S., 1993. Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4–5), pp.185–196.

[7]. Muhammad, A.R.,, Utomo, H.P.,, Hidayatullah, P., and Syakrani, N., 2022. Early stopping effectiveness for Yolov4. Journal of Information Systems Engineering and Business Intelligence, 8(1), pp.11–20.

[8]. Xu-hui, C.,, Haq, E.U., and Chengyu, Z., 2019. Notice of violation of IEEE Publication Principles: Efficient Technique to accelerate neural network training by freezing hidden layers. 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS).

[9]. Xu, Y.,, Wang, H.,, Liu, X., and Sun’s, W., 2019. An improved multi-branch residual network based on random multiplier and adaptive cosine learning rate method. Journal of Visual Communication and Image Representation, 59, pp.363–370.

[10]. Zhu, A.,, Meng, Y., and Zhang, C., 2017. An improved adam algorithm using look-ahead. Proceedings of the 2017 International Conference on Deep Learning Technologies.

Cite this article

Yu,F. (2024). YOLO, Faster R-CNN, and SSD for cloud detection. Applied and Computational Engineering,37,239-247.

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 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-299-2(Print) / 978-1-83558-300-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.37
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
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References

[1]. Amit, Y.,, Felzenszwalb, P., and Girshick, R., 2020. Object detection. Computer Vision, pp.1–9.

[2]. Zou, Z.,, Chen, K.,, Shi, Z.,, Guo, Y., and Ye, J., 2023. Object detection in 20 years: A survey. Proceedings of the IEEE, 111(3), pp.257–276.

[3]. Lee, J.,, Weger, R.C.,, Sengupta, S.K., and Welch, R.M., 1990. A neural network approach to cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 28(5), pp.846–855.

[4]. Baghel, V.S.,, Srivastava, A.M.,, Prakash, S., and Singh, S., 2020. Minutiae points extraction using faster R-CNN. Advances in Intelligent Systems and Computing, pp.3–10.

[5]. Li, R., and Wu, Y., 2022. Improved Yolo V5 wheat ear detection algorithm based on attention mechanism. Electronics, 11(11), p.1673.

[6]. Amari, S., 1993. Backpropagation and stochastic gradient descent method. Neurocomputing, 5(4–5), pp.185–196.

[7]. Muhammad, A.R.,, Utomo, H.P.,, Hidayatullah, P., and Syakrani, N., 2022. Early stopping effectiveness for Yolov4. Journal of Information Systems Engineering and Business Intelligence, 8(1), pp.11–20.

[8]. Xu-hui, C.,, Haq, E.U., and Chengyu, Z., 2019. Notice of violation of IEEE Publication Principles: Efficient Technique to accelerate neural network training by freezing hidden layers. 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS).

[9]. Xu, Y.,, Wang, H.,, Liu, X., and Sun’s, W., 2019. An improved multi-branch residual network based on random multiplier and adaptive cosine learning rate method. Journal of Visual Communication and Image Representation, 59, pp.363–370.

[10]. Zhu, A.,, Meng, Y., and Zhang, C., 2017. An improved adam algorithm using look-ahead. Proceedings of the 2017 International Conference on Deep Learning Technologies.

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