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Related Experiment Video

Updated: May 26, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
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YOLO-DHGC: Small Object Detection Using Two-Stream Structure with Dense Connections.

Lihua Chen1, Lumei Su1,2, Weihao Chen1

  • 1School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO-DHGC, a novel method for small object detection. It achieves high accuracy in defect detection by enhancing feature reuse and focusing on object boundaries.

Keywords:
dense connectionsmall object detectiontwo-stream structure

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Small object detection is crucial for applications like defect detection and medical imaging.
  • Existing methods struggle with low accuracy due to limited features and blurred details in small objects.

Purpose of the Study:

  • To propose an effective small object detection method, YOLO-DHGC, to address accuracy limitations.
  • To enhance the detection of small objects by improving feature extraction and focusing on shape boundaries.

Main Methods:

  • Introduced DenseHRNet, a novel backbone network combining dense connections and high-resolution feature maps for enhanced feature reuse and fusion.
  • Designed a two-stream structure with an edge-gated branch, utilizing higher-level information to refine focus on object boundaries and morphological features.
  • Validated the YOLO-DHGC method on public and self-constructed datasets.

Main Results:

  • Achieved a 96.3% defect detection accuracy on the Market-PCB public dataset.
  • Demonstrated significant effectiveness in detecting small object defects for industrial applications.
  • The proposed method successfully captures morphological features of small objects.

Conclusions:

  • YOLO-DHGC effectively improves small object detection accuracy by enhancing feature representation and boundary information.
  • The novel DenseHRNet backbone and edge-gated stream contribute to superior performance in challenging detection scenarios.
  • The method shows strong potential for industrial defect detection and other small object detection applications.