Small Pedestrian Target Detection in Subway Scene Based on Improved SSD Algorithm

Zhang Xiuzai; Qiu Ye; Shen Tao

doi:10.7544/issn1000-1239.202330069

Journal of Computer Research and Development > 2025 > 62(2): 397-407. > DOI: 10.7544/issn1000-1239.202330069 CSTR: 32373.14.issn1000-1239.202330069

Zhang Xiuzai, Qiu Ye, Shen Tao. Small Pedestrian Target Detection in Subway Scene Based on Improved SSD Algorithm[J]. Journal of Computer Research and Development, 2025, 62(2): 397-407. DOI: 10.7544/issn1000-1239.202330069

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Small Pedestrian Target Detection in Subway Scene Based on Improved SSD Algorithm

Zhang Xiuzai^{1, 2,},
Qiu Ye¹,
Shen Tao¹

1.
School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044
2.
Jiangsu Provincial Atmospheric Environment and Equipment Technology Collaborative Innovation Center (Nanjing University of Information Science & Technology), Nanjing 210044

Funds: This work was supported by the Natural Science Foundation of Jiangsu Provincial Higher Education Institutions (13KJA510001), the Natural Science Foundation of Jiangsu Province for Young Scientists (BK20141004), and the National Natural Science Foundation of China for Young Scientists (11504176, 61601230).

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Author Bio:
Zhang Xiuzai: born in 1979. PhD. His main research interests include meteorological communication technology and security, and machine learning

Qiu Ye: born in 1995. Master. His main research interests include image processing and object detection

Shen Tao: born in 2000. Master. His main research interests include deep learning and object detection
Received Date: February 05, 2023
Revised Date: May 12, 2024
Accepted Date: May 29, 2024
Available Online: June 30, 2024

Graphical Abstract

Abstract

Abstract

In the subway scene, small pedestrian targets contain less feature information due to their low resolution, and it is still challenging for object detectors to detect such objects at this stage. SSD target detection algorithm uses the multi-scale detection head of the pyramid network, which can improve the pedestrian target detection performance to a certain extent, but it still has certain limitations in small pedestrian target detection application in complex environments such as subways. In view of the above problems, we propose an improved SSD algorithm to enhance the detection effect of small pedestrian targets in subway scenes, construct a dataset of pedestrian targets in subway scenes, mark the corresponding labels, and perform data preprocessing operations at the same time. In this study, a pyramid feature enhancement module is added to the feature extraction network, and the multi-branch residual unit, sub-pixel convolution and feature pyramid are combined to obtain image multi-scale and multi-receptive field fusion features. We use the context information fusion module to fuse the low-level features of the image with the context features to generate an extended feature layer for detecting small pedestrian targets, and design an Anchor-free dynamic positive and negative sample allocation strategy to generate optimal positive samples for small pedestrian targets. A dynamic positive and negative sample allocation strategy based on Anchor-free is designed to generate optimal positive samples for small pedestrian targets. The experimental results show that the proposed improved SSD algorithm can effectively improve the performance of small pedestrian target detection in subway scenes, and the effect of small pedestrian target detection with severe occlusion is more obvious.
- small pedestrian object detection,
- SSD algorithm,
- attention mechanism,
- subpixel convolution,
- multi-scale feature fusion

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References (19)

References

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