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Open AccessArticle

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

1
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
3
Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310058, China
4
Faculty of Engineering, Kitami Institute of Technology, Koen-cho 165, Kitami, Hokkaido 090-8507, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4082; https://doi.org/10.3390/s20154082
Received: 2 June 2020 / Revised: 18 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
(This article belongs to the Section Remote Sensors)
Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation. View Full-Text
Keywords: machine vision; deep learning; detecting and tracking; moving obstacles; paddy field machine vision; deep learning; detecting and tracking; moving obstacles; paddy field
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Qiu, Z.; Zhao, N.; Zhou, L.; Wang, M.; Yang, L.; Fang, H.; He, Y.; Liu, Y. Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT. Sensors 2020, 20, 4082.

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