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Improved YOLO Based Detection Algorithm for Floating Debris in Waterway.

Feng Lin1, Tian Hou1, Qiannan Jin2

  • 1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.

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|September 28, 2021
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Summary
This summary is machine-generated.

A new FMA-YOLOv5s algorithm improves real-time water quality monitoring by accurately detecting floating debris. This method enhances detection of small targets and meets real-time object detection standards.

Keywords:
YOLOv5deep learningdetection algorithmfloating debris detection

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Area of Science:

  • Environmental Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Floating debris in waterways serves as a visual indicator of water quality.
  • Traditional image processing struggles with real-time monitoring due to environmental complexities like sunlight reflection, aquatic plants, and varying target scales.

Purpose of the Study:

  • To develop an improved algorithm for real-time detection of floating debris in waterways.
  • To enhance the accuracy and speed of water quality monitoring using visual data.

Main Methods:

  • An improved YOLOv5s algorithm (FMA-YOLOv5s) was proposed, incorporating a feature map attention (FMA) layer.
  • Mosaic data augmentation was employed to improve the detection of small targets.
  • A data expansion technique increased the training dataset size from 1920 to 4800 samples, integrating labeled objects with clean river background images.

Main Results:

  • The FMA-YOLOv5s algorithm demonstrated improved accuracy and rapidity compared to six other models.
  • The enhanced dataset and FMA layer contributed to better detection performance.
  • The algorithm successfully met the requirements for real-time object detection.

Conclusions:

  • The FMA-YOLOv5s algorithm offers a viable solution for real-time monitoring of floating debris.
  • This approach significantly advances the capability of using visual indices for water quality assessment.
  • The study validates the effectiveness of the proposed algorithm in complex environmental conditions.