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Updated: May 10, 2025

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HPRT-DETR: A High-Precision Real-Time Object Detection Algorithm for Intelligent Driving Vehicles.

Xiaona Song1, Bin Fan1, Haichao Liu1

  • 1School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Sensors (Basel, Switzerland)
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PubMed
Summary

This study introduces HPRT-DETR, an improved object detection algorithm for intelligent vehicles that enhances accuracy for occluded and small targets. It significantly boosts performance over RT-DETR, addressing key limitations in real-time perception systems.

Keywords:
RT-DETRdeformable attentionintelligent driving vehiclesmulti-scale fusionsmall target detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Driving Systems

Background:

  • Object detection is critical for autonomous vehicle perception.
  • Existing models like RT-DETR struggle with occluded or small targets in intelligent driving.
  • There is a need for more robust and precise real-time object detection algorithms.

Purpose of the Study:

  • To propose a High-Precision Real-Time object detection algorithm (HPRT-DETR) for intelligent driving vehicles.
  • To address the misdetection issues of occluded and small targets faced by current models.
  • To improve the overall accuracy and efficiency of object detection in complex driving scenarios.

Main Methods:

  • Developed a Basic-iRMB-CGA (BIC) Block for efficient feature extraction and parameter reduction in the backbone network.
  • Introduced a Deformable Attention-based Intra-scale Feature Interaction (DAIFI) module to capture rich semantic features and improve occlusion handling.
  • Integrated Local Feature Extraction Fusion (LFEF) with CNN-based Cross-scale Feature Fusion (CCFF) to expand receptive fields and enhance small target detection without added complexity.

Main Results:

  • HPRT-DETR demonstrated a 1.98% improvement in mAP50 and a 15.25% increase in FPS compared to RT-DETR on the KITTI dataset.
  • The algorithm showed superior performance across most evaluation metrics on the SODA 10M dataset, indicating strong generalization.
  • The proposed BIC Block, DAIFI module, and LFEF block effectively addressed the limitations of detecting occluded and small objects.

Conclusions:

  • HPRT-DETR offers a significant advancement in real-time object detection for intelligent driving systems.
  • The novel architectural components contribute to enhanced feature extraction and improved accuracy in challenging conditions.
  • The algorithm's effectiveness and generalization capabilities make it a promising solution for autonomous vehicle perception.