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Reseach Article

Object Detection Algorithms Compression CNN, YOLO and SSD

by Shreyas Pagare, Rakesh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 7
Year of Publication: 2023
Authors: Shreyas Pagare, Rakesh Kumar
10.5120/ijca2023922726

Shreyas Pagare, Rakesh Kumar . Object Detection Algorithms Compression CNN, YOLO and SSD. International Journal of Computer Applications. 185, 7 ( May 2023), 34-38. DOI=10.5120/ijca2023922726

@article{ 10.5120/ijca2023922726,
author = { Shreyas Pagare, Rakesh Kumar },
title = { Object Detection Algorithms Compression CNN, YOLO and SSD },
journal = { International Journal of Computer Applications },
issue_date = { May 2023 },
volume = { 185 },
number = { 7 },
month = { May },
year = { 2023 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number7/32716-2023922726/ },
doi = { 10.5120/ijca2023922726 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:31.445272+05:30
%A Shreyas Pagare
%A Rakesh Kumar
%T Object Detection Algorithms Compression CNN, YOLO and SSD
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 7
%P 34-38
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object detection is a crucial component of computer vision, and since 2015, several studies have expanded with the use of convolution neural networks and their changed structures. There are techniques for detecting representative objects, including YOLO and convolutional neural networks and also use SSD. This study introduces three exemplary CNN and YOLO-based and SSD algorithm series that address the CNN bounding box issue. We examine the accuracy, speed, and cost of many algorithmic series. All model of YOLO provides an excellent balance between speed and accuracy when compared to the most recent advanced solution.

References
  1. Asifullah Khan, Anabia Sohail, Umme Zahoora, AqsaSaeed Qureshi, “A Survey of the Recent Architectures of Deep Convolutional Neural Networks”, Computer Vision and Pattern Recognition, Available at https://arxiv.org/ftp/arxiv/papers/1901/1901.06032.pdf [Accessed Mar. 13, 2020].
  2. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, “Rich Feature Hierarchies for ccurate Object Detection and Semantic Segmentation”, IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587, 2013.
  3. Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, Matti Pietikainen, “Deep
  4. Learning for Generic Object Detection: A Survey”, International Journal of Computer Vision, vol.128, pp.261-318 2020.
  5. Kaimin He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Spatial Pyramid Pooling in Deep Convolutional
  6. Networks for Visual Recognition”, European Conference on Computer Vision, Part 3, pp.346-361,2014.
  7. Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhizi Feng, Rong Qu, “A Survey of Deep
  8. Learning-based Object Detection”, IEEE Access, vol.7, pp.128837-128868, , 2019.
  9. David G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, vol.60, pp.91-110, 2004.
  10. Juan Du, “Understanding of Object Detection based on CNN Family and YOLO”, Journal of Physics,
  11. Conference Series, vol.1004, issue.1, 2018.
  12. Tsung-Yi Lin, Priya Goyal, Roos Girshick, Kaiming He, Piotr Dollar, “Focal Loss for Dense Object Detection”, International Conference on Computer Vision, pp.2999- 3007, 2017.
  13. Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool, “Speeded-Up Robust Features (SURF)”, Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.
  14. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Communications of the ACM, vol.60, no.6, 2017.
  15. Yurong Yang, Huajun Gong, Xinhua Wang, Peng Sun, “Aerial Target Tracking Algorithm Based on Faster RCNN Combined with Frame Differencing”, Aerospace, vol.4, no.32, 2017.
  16. Kwanghyun Kim, Sungjun Hong, Baehoon Choi and Euntai Kim, “Probabilistic Ship Detection and
  17. Classification using Deep Learning”, Applied Sciences, vol.8, no.6, 2018.
  18. Rohith Gandhi, “R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms,” 2018. Available at https://towardsdatascience.com/r-cnn-fast-r-cnn-fasterr- cnn-yolo-object-detection-algorithms-36d53571365e. [Accessed: Mar. 13, 2020].
  19. N. Dalal, B. Triggs, “Histograms of Oriented Gradients
  20. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection” ,2020.
  21. Mingxing Tan Ruoming Pang Quoc V. Le Google Research, Brain Team. “EfficientDet: Scalable and Efficient Object Detection”. 2020
  22. Chien-Yao Wang , Alexey Bochkovskiy, and Hong-Yuan Mark Liao, Institute of Information Science, Academia Sinica, Taiwan, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. 2022.
  23. https://jonathan-hui.medium.com/object-detection-speed-and-accuracy-comparison-faster-r-cnn-r-fcn-ssd-and-yolo-5425656ae359
Index Terms

Computer Science
Information Sciences

Keywords

CNN YOLO SSD