Comparing one-stage and two-stage learning strategy in object detection

Research Article
Open access

Comparing one-stage and two-stage learning strategy in object detection

Hanqing Bi 1* , Vincent Wen 2 , Zhenyu Xu 3
  • 1 Tianjin No.21 High School, Tianjin, 300400, China    
  • 2 The Affiliated High School of Fuzhou Institute of Education, Fuzhou, Fujian, 350108 China    
  • 3 International Department, The Affiliated High School of SCNU, Guangzhou, Guangdong, 510000, China    
  • *corresponding author g04@xsy.edu.pl
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Object detection plays a vital role in computer social perception and computer vision. It could be applied to computer navigation, video monitoring, industrial detection, and so on. It greatly reduces the human labours by automatically locate and identify objects. Nowadays, the mainstream methods of object detection could be separated into the one- and two-stage method. The one-stage method leverages Convolutional Neural Network (CNN) for obtaining features and directly locate the target objects and their corresponding category probabilities. Different from the two-stage solutions, its accuracy is lower and the recognition speed is higher. The two-stage method is a straight forward solution, which process is mainly completed through a complete CNN, so CNN features will be leveraged to extract the feature description of the target among candidate regions through a CNN. The accuracy of the two-step method has been greatly improved, but the running speed is much slower than the one-step method. While the one-step method is less accurate, it is much faster. In this work, representative works for object detection are conducted and compared. The results could further demonstrate their respective advantages.

Keywords:

Object Detection, Deep Learning, One-stage Learning, Two-stage Learning.

Bi,H.;Wen,V.;Xu,Z. (2023). Comparing one-stage and two-stage learning strategy in object detection. Applied and Computational Engineering,5,171-177.
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References

[1]. Redmon J, Divvala S, Girshick R, et al., 2016, You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp: 779-788.

[2]. Redmon J, Farhadi A., 2017, YOLO9000: better, faster, stronger, In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7263-7271.

[3]. Redmon J, Farhadi A., 2018, Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

[4]. Bochkovskiy A, Wang C Y, Liao H Y M., 2020, Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

[5]. Ge Z, Liu S, Wang F, et al., 2021, Yolox: Exceeding yolo series in 2021[J]. arXiv preprint arXiv:2107.08430.

[6]. Lin T Y, Goyal P, Girshick R, et al., 2017, Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980-2988.

[7]. Law H, Deng J., 2018, Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision, pp 734-750.

[8]. Duan K, Bai S, Xie L, et al., 2019, Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 6569-6578.

[9]. Tan M, Pang R, Le Q V., 2020, Efficientdet: Scalable and efficient object detection, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 10781-10790.

[10]. Girshick R, Donahue J, Darrell T, et al., 2014, Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 580-587.

[11]. Ren S, He K, Girshick R, et al., 2015, Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, 28.

Cite this article

Bi,H.;Wen,V.;Xu,Z. (2023). Comparing one-stage and two-stage learning strategy in object detection. Applied and Computational Engineering,5,171-177.

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 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Redmon J, Divvala S, Girshick R, et al., 2016, You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp: 779-788.

[2]. Redmon J, Farhadi A., 2017, YOLO9000: better, faster, stronger, In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7263-7271.

[3]. Redmon J, Farhadi A., 2018, Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

[4]. Bochkovskiy A, Wang C Y, Liao H Y M., 2020, Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

[5]. Ge Z, Liu S, Wang F, et al., 2021, Yolox: Exceeding yolo series in 2021[J]. arXiv preprint arXiv:2107.08430.

[6]. Lin T Y, Goyal P, Girshick R, et al., 2017, Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp 2980-2988.

[7]. Law H, Deng J., 2018, Cornernet: Detecting objects as paired keypoints. In: Proceedings of the European conference on computer vision, pp 734-750.

[8]. Duan K, Bai S, Xie L, et al., 2019, Centernet: Keypoint triplets for object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 6569-6578.

[9]. Tan M, Pang R, Le Q V., 2020, Efficientdet: Scalable and efficient object detection, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 10781-10790.

[10]. Girshick R, Donahue J, Darrell T, et al., 2014, Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 580-587.

[11]. Ren S, He K, Girshick R, et al., 2015, Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, 28.

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