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Related Experiment Videos

RMP-YOLO: Robust Multi-Scale Pedestrian Detection for Dense Scenarios.

Chenyang Gui1,2, Zhangyu Fan3, Taibin Duan4

  • 1School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces RMP-YOLO, a lightweight algorithm for robust pedestrian detection, excelling at identifying small, occluded, or low-light individuals. It significantly enhances accuracy in crowded scenes while maintaining computational efficiency for autonomous driving systems.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous driving systems require advanced pedestrian detection.
  • Current dense pedestrian detection methods face performance limitations, especially with small, occluded, or low-light objects.

Purpose of the Study:

  • To develop a robust and lightweight pedestrian detection algorithm (RMP-YOLO) for autonomous driving.
  • To improve the detection of challenging pedestrian scenarios like small, occluded, and low-light conditions.

Main Methods:

  • Utilized RFAConv in the backbone network, combining standard convolution, attention mechanisms, and group convolution for feature extraction.
  • Integrated MobileViTv3 to merge Convolutional Neural Networks (CNNs) with Transformers, enhancing feature fusion and local representation.
Keywords:
MobileViTv3PIoUv2RFAConvhigh precision

Related Experiment Videos

  • Employed the PIoUv2 loss function for precise bounding-box regression, particularly for small pedestrians.

    • Main Results:

    • RMP-YOLO achieved a 1.3% mAP@0.5 improvement on a custom dataset and 0.91% on the WiderPerson dataset.
    • The algorithm demonstrates high efficiency with only 3.71 million parameters and 6.29 GFLOPs.
    • Significantly reduced detection errors for small-scale pedestrians in crowded environments.

    Conclusions:

    • RMP-YOLO offers a computationally efficient and accurate solution for dense pedestrian detection in autonomous driving.
    • The proposed method effectively addresses performance bottlenecks in detecting challenging pedestrian targets.
    • Meets deployment requirements for systems with limited computational power and high precision demands.