@article{oai:kitami-it.repo.nii.ac.jp:02000438,
 author = {Zhengjun Qiu and Nan Zhao and Lei Zhou and Mengcen Wang and Liangliang Yang and Hui Fang and Yong He and Yufei Liu},
 issue = {15},
 journal = {Sensors},
 month = {Jul},
 note = {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.},
 pages = {4082--4082},
 title = {Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT},
 volume = {20},
 year = {2020}
}