YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3
Guoxu Liu1,2,  Joseph Christian Nouaze2,  Philippe Lyonel Touko2,  Jae Ho Kim2 
1 Weifang University of Science and Technology, China
2 Pusan National University, Korea
[Paper]
Overview
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.
Results
We perform an ablation analysis of dense architecture and C-Bbox.
The network visualization.
BibTeX
@article{liu2020yolo,
  title   = {YOLO-Tomato: A Robust Algorithm for Tomato Detection based on YOLOv3},
  author  = {Liu, Guoxu and Nouaze, Joseph Christian and Touko, Philippe Lyonel and Kim, Jae Ho},
  journal = {Sensors},
  volume  = {20},
  number  = {7},
  pages   = {2145},
  year    = {2020},
  doi     = {10.3390/s20072145}
}
Related Work
G. Liu, S. Mao, J.H. Kim. A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis. Sensors, 19(9), 2019.
Comment: Detect tomatoes using a coarse-to-fine framework based on the combination of HOG, SVM, and a color analysis method.