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A lightweight network face detection based on YOLOv5

Published: 28 June 2024 Publication History

Abstract

Face detection is a mainstream way of human identification today, but the current object detection network requires a large number of parameters and calculations, in response to this problem, we propose a lightweight improved face detection method based on YOLOv5, which can be easily deployed in embedded devices and mobile devices. We replaced YOLOv5s backbone with a more lightweight MobileNet, replacing standard convolution with deep separable convolution; Then we also use the SIoU loss function to speed up the convergence speed of the prediction box and the target box, improving the inference rate. Compared with the baseline model, the YOLOv5-MS model reduces the number of parameters by 91.4% and increases FPS by 62.5%, which achieves a good balance between detection accuracy and rate.

References

[1]
Girshick R, Donahue J, Darrell T, Rich feature hierarchies for accurate object detection and semantic segmentation[C].Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 580-587.
[2]
He K, Zhang X, Ren S, Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916.
[3]
Ren S, He K, Girshick R, Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28
[4]
Redmon J, Divvala S, Girshick R, You only look once: Unified, real-time object detection[C].Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
[5]
Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.
[6]
Redmon, J; Farhadi, A. Yolov3: An incremental improvement. arXiv 2018, arXiv:1804.02767.
[7]
Bochkovskiy, A; Wang, C.-Y; Liao, H.-Y.M. Yolov4: Optimal speed and accuracy of object detection. arXiv 2020, arXiv:2004.10934.
[8]
Liu W, Anguelov D, Erhan D, Ssd: Single shot multibox detector[C].Computer Vision–ECCV, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.
[9]
A. Gholami,“SqueezeNext: Hardware-aware neural net-work design,” 2018 IEEE/CVF Conference on Computer Visionand Pattern RecognitionWorkshops (CVPRW), 2018, pp. 1719–171909.
[10]
X. Zhang, X. Zhou, M. Lin, and J. Sun,“ShuffleNet: An extremely efficient convolutional neural network for mobile devices,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 6848–6856.
[11]
N. Ma, X. Zhang, H.T. Zheng, and J. Sun,“Shufflenet v2: Practical guidelines for efficient CNN architecture design,” in Proc.16th European Conference on Computer Vision (ECCV), 2018,pp. 122–138.
[12]
A.G. Howard, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv, 2017.
[13]
M. Sandler,“Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proc. 36th IEEE conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 4510–4520.
[14]
A. Howard, “Searching for mobilenetv3,” in Proc. 17th IEEE Int. Conf. Comput. Vis. (ICCV), 2019, pp. 1314–1324.
[15]
Gevorgyan Z. SIoU loss: More powerful learning for bounding box regression[J]. arXiv preprint arXiv:2205.12740, 2022.

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ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
September 2023
335 pages
ISBN:9798400708039
DOI:10.1145/3655532
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2024

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Author Tags

  1. Lightweight network
  2. MobileNetv2
  3. Object detection
  4. SIoU
  5. YOLOv5

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