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A novel algorithm for small object detection based on YOLOv4.

Jiangshu Wei1, Gang Liu1, Siqi Liu1

  • 1College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

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Summary
This summary is machine-generated.

This study enhances small object detection using a modified YOLOv4 network, improving accuracy in complex environments like drone imagery and road scenes. The new model offers better performance with fewer parameters for real-time applications.

Keywords:
Attention mechanismsConvolution neural networkDeep learningFeature fusionSmall object detectionYOLOv4

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Object Detection

Background:

  • Small object detection is challenging due to complex backgrounds, noise, and occlusion.
  • Traditional methods struggle with accuracy in real-world scenarios like aerial surveys and road monitoring.

Purpose of the Study:

  • To develop an improved small object detection network based on YOLOv4.
  • To enhance accuracy and efficiency for detecting small objects in complex environments.

Main Methods:

  • Incorporated Cross-Stage Partial Network (CSPNet) into the spatial pyramid pool (SPP) structure.
  • Introduced a dedicated small object detection head and a shallow feature extraction branch.
  • Integrated a weighting mechanism for feature fusion and a coordinated attention (CA) module.

Main Results:

  • Achieved 52.76% mAP on a drone aerial dataset, outperforming YOLOv4 and YOLOv5L.
  • Reached 96.98% accuracy on a road traffic light dataset, surpassing existing models.
  • Demonstrated real-time detection speed with only 44M parameters.

Conclusions:

  • The proposed YOLOv4-based network significantly improves small object detection accuracy in complex scenes.
  • The model offers an efficient and effective solution for applications like drone surveillance and autonomous driving.