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Small object intelligent detection method based on adaptive recursive feature pyramid.

Jie Zhang1, Hongyan Zhang1, Bowen Liu1

  • 1College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.

Heliyon
|August 4, 2023
PubMed
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This study introduces an adaptive recursive path aggregation network (AR-PANet) to enhance YOLOv4

Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • YOLOv4 demonstrates strong object detection capabilities but struggles with small object detection due to scale inconsistencies in its Path Aggregation Network (PANet).
  • Inaccurate detection of small objects limits applications in fields like remote sensing and intelligent transportation.

Purpose of the Study:

  • To improve the detection accuracy of small objects within the YOLOv4 framework.
  • To address the limitations of PANet in handling multi-scale features for small object recognition.

Main Methods:

  • Implemented an adaptive recursive path aggregation network (AR-PANet) by feeding PANet outputs back to the backbone network.
  • Developed an adaptive approach to mitigate conflicting information in multi-scale feature spaces, enhancing scale invariance.
Keywords:
AR-PANetAdaptive methodCBAMRecursive structureSmall object detection

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  • Utilized the Convolutional Block Attention Module (CBAM) for feature refinement by mapping multi-scale features to independent channels and spatial dimensions.

    • Main Results:

    • The proposed AR-PANet significantly improves the accuracy of small object detection across multiple datasets.
    • Experimental results demonstrate impressive performance gains in addressing the challenge of small object detection.

    Conclusions:

    • The AR-PANet effectively enhances YOLOv4's capability for detecting small objects.
    • This approach shows significant potential for applications in remote sensing and intelligent transportation systems.