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ACF Based Region Proposal Extraction for YOLOv3 Network Towards High-Performance Cyclist Detection in High Resolution

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  • 1School of Control Science and Engineering, Shandong University, Ji'nan 250061, China. liuchunsheng@sdu.edu.cn.

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|June 16, 2019
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Summary
This summary is machine-generated.

The proposed ACF-PR-YOLO method enhances object detection in high-resolution images by improving small object detection. This approach significantly boosts average precision compared to existing YOLO and SSD models.

Keywords:
You Only Look Once (YOLO)aggregated channel feature (ACF)cyclist detectionregion proposal extraction

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Object Detection

Background:

  • You Only Look Once (YOLO) networks excel at fast, precise object detection.
  • YOLO networks struggle with high precision for small objects in high-resolution images.

Purpose of the Study:

  • To enhance YOLO's precision for small object detection in high-resolution images.
  • To introduce an effective region proposal extraction method for YOLO.

Main Methods:

  • Developed ACF-PR-YOLO, integrating aggregated channel features (ACF) for region proposal extraction (ACF-PR).
  • ACF-PR generates large potential object regions, merging and extending bounding boxes for YOLO.
  • A tailored YOLO network and post-processing step refine detection within proposed regions.

Main Results:

  • ACF-PR-YOLO demonstrated superior performance on the Tsinghua-Daimler Cyclist Benchmark.
  • Achieved 13.69% higher average precision than YOLOv3.
  • Outperformed SSD by 25.27% in average precision for cyclist detection.

Conclusions:

  • The proposed ACF-PR-YOLO method effectively addresses YOLO's limitations in small object detection.
  • This approach offers significant improvements in average precision for complex, high-resolution datasets.