A Vehicle Re-Identification Algorithm for Long-Distance Small Targets Combining YOLOv8 Object Detection Algorithm

Authors

  • Chenyu Gu
  • Hong Du
  • Xiaozheng Zhang
  • Ying Wang
  • Zhonglin Yang
  • Gaotian Liu
  • Chuan Zhang
  • Lei Sun

DOI:

https://doi.org/10.54097/6msfv079

Keywords:

Vehicle Re-identification, Object Detection, Attention Mechanism

Abstract

With the rapid development of intelligent technologies, deep learning-based object detection has been widely applied in dynamic fields, especially in vehicle management. However, challenges in accurate recognition and tracking remain, particularly due to issues like intra-class variations and inter-class similarities in vehicle re-identification across camera viewpoints. This paper proposes a method for long-range small vehicle target re-identification based on the YOLOv8 object detection algorithm, aiming to improve the detection accuracy and re-identification performance of small targets in complex environments. First, a detection and re-identification dataset containing long-range vehicle images is constructed based on a complex background and small target dataset. Secondly, the YOLOv8-EMA-RFB optimization algorithm is introduced, which combines the EMA (Exponential Moving Average) attention mechanism and RFB (Receptive Field Block) structure. The EMA module enhances feature extraction for small targets and reduces noise interference, while the RFB module increases the receptive field, improving the detection capability for small targets. Through these optimizations, the proposed method effectively improves the detection accuracy of long-range small targets in the YOLOv8 model. Additionally, by incorporating the AlignedReID method, feature matching in object re-identification is improved, further enhancing the accuracy and robustness of multi-object tracking. Experimental results demonstrate that the proposed method achieves high precision and real-time performance in multi-scene vehicle detection and re-identification, offering a novel solution for intelligent transportation and urban security surveillance.

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References

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Published

21-01-2025

Issue

Section

Articles

How to Cite

Gu, C., Du, H., Zhang, X., Wang, Y., Yang, Z., Liu, G., Zhang, C., & Sun, L. (2025). A Vehicle Re-Identification Algorithm for Long-Distance Small Targets Combining YOLOv8 Object Detection Algorithm. Frontiers in Computing and Intelligent Systems, 11(1), 53-58. https://doi.org/10.54097/6msfv079