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Article

YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3

1
Computer Software Institute, Weifang University of Science and Technology, Shouguang 262-700, China
2
Department of Electronics Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2145; https://doi.org/10.3390/s20072145
Received: 11 March 2020 / Revised: 1 April 2020 / Accepted: 7 April 2020 / Published: 10 April 2020
(This article belongs to the Section Intelligent Sensors)
Automatic fruit detection is a very important benefit of harvesting robots. However, complicated environment conditions, such as illumination variation, branch, and leaf occlusion as well as tomato overlap, have made fruit detection very challenging. In this study, an improved tomato detection model called YOLO-Tomato is proposed for dealing with these problems, based on YOLOv3. A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model. Moreover, the model replaces the traditional rectangular bounding box (R-Bbox) with a circular bounding box (C-Bbox) for tomato localization. The new bounding boxes can then match the tomatoes more precisely, and thus improve the Intersection-over-Union (IoU) calculation for the Non-Maximum Suppression (NMS). They also reduce prediction coordinates. An ablation study demonstrated the efficacy of these modifications. The YOLO-Tomato was compared to several state-of-the-art detection methods and it had the best detection performance. View Full-Text
Keywords: tomato detection; harvesting robots; dense architecture; deep learning tomato detection; harvesting robots; dense architecture; deep learning
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MDPI and ACS Style

Liu, G.; Nouaze, J.C.; Touko Mbouembe, P.L.; Kim, J.H. YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3. Sensors 2020, 20, 2145. https://doi.org/10.3390/s20072145

AMA Style

Liu G, Nouaze JC, Touko Mbouembe PL, Kim JH. YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3. Sensors. 2020; 20(7):2145. https://doi.org/10.3390/s20072145

Chicago/Turabian Style

Liu, Guoxu, Joseph C. Nouaze, Philippe L. Touko Mbouembe, and Jae H. Kim. 2020. "YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3" Sensors 20, no. 7: 2145. https://doi.org/10.3390/s20072145

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