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Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

1
Graduate School of Life and Environmental Sciences, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8577, Japan
2
Faculty of Life and Environmental Sciences, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8577, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Jong-Jae Lee
Sensors 2021, 21(14), 4803; https://doi.org/10.3390/s21144803
Received: 10 June 2021 / Revised: 28 June 2021 / Accepted: 8 July 2021 / Published: 14 July 2021
(This article belongs to the Section Remote Sensors)
This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an [email protected] of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8–14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected. View Full-Text
Keywords: YOLO; YOLOv4; Deep SORT; object counting; real time; object detection; fruit detection YOLO; YOLOv4; Deep SORT; object counting; real time; object detection; fruit detection
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MDPI and ACS Style

Parico, A.I.B.; Ahamed, T. Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT. Sensors 2021, 21, 4803. https://doi.org/10.3390/s21144803

AMA Style

Parico AIB, Ahamed T. Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT. Sensors. 2021; 21(14):4803. https://doi.org/10.3390/s21144803

Chicago/Turabian Style

Parico, Addie I.B., and Tofael Ahamed. 2021. "Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT" Sensors 21, no. 14: 4803. https://doi.org/10.3390/s21144803

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