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Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT.

Zhengjun Qiu1,2, Nan Zhao1,2, Lei Zhou1,2

  • 1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

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|July 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) system for detecting and tracking moving obstacles in paddy fields. The enhanced system achieves faster processing speeds, supporting intelligent agricultural machine navigation.

Keywords:
deep learningdetecting and trackingmachine visionmoving obstaclespaddy field

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Robotics

Background:

  • Intelligent agricultural machines require robust obstacle avoidance systems for autonomous operation in complex environments like paddy fields.
  • Detecting and tracking moving obstacles is crucial for enhancing the intelligence and safety of agricultural machinery.

Purpose of the Study:

  • To develop and evaluate a machine vision system for detecting and tracking moving obstacles in paddy fields.
  • To improve the performance and processing speed of obstacle detection and tracking for agricultural robots.

Main Methods:

  • A machine vision system was developed using a red, green, and blue (RGB) camera and computer, mounted on a transplanter.
  • An improved You Only Look Once version 3 (Yolov3) algorithm combined with deep Simple Online and Realtime Tracking (deep SORT) was utilized for obstacle detection and tracking.
  • The improved Yolov3 features architectural modifications, including 23 residual blocks and single upsampling, along with novel loss calculation functions.

Main Results:

  • The improved Yolov3 achieved a mean intersection over union (mIoU) score of 0.779 on a custom dataset of moving obstacles (humans, water buffalo).
  • The system demonstrated a 27.3% increase in processing speed compared to the standard Yolov3.
  • Real-world paddy field tests showed an average processing speed of 5-7 frames per second (FPS), meeting operational demands.

Conclusions:

  • The proposed machine vision system effectively detects and tracks moving obstacles in paddy fields, enhancing agricultural machine intelligence.
  • The improved Yolov3-deep SORT method offers a significant speed advantage and acceptable detection accuracy for practical applications.
  • This system provides a foundation for more flexible autonomous navigation in intelligent agriculture.