DeepFruits: A Fruit Detection System Using Deep Neural Networks.
DeepFruits: A Fruit Detection System Using Deep Neural Networks.
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DOI：
10.3390/s16081222
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发表时间：
2016-08-03
期刊：
影响因子：
--
通讯作者：
McCool C
中科院分区：
文献类型：
--
作者：
Sa I;Ge Z;Dayoub F;Upcroft B;Perez T;McCool C
本文提出了一种使用深度卷积神经网络进行水果检测的新方法。其目的是建立一个准确，快速和可靠的水果检测系统，这是一个自主农业机器人平台的重要组成部分;它是水果产量估计和自动化收获的关键因素。最近在深度神经网络方面的工作导致了一种最先进的对象检测器的开发，称为基于区域的更快CNN（Faster R-CNN）。我们通过迁移学习调整了这个模型，用于使用从两种模式获得的图像进行水果检测的任务：颜色（RGB）和近红外（NIR）。探索了早期和后期融合方法来组合多模态（RGB和近红外）信息。这导致了一种新型的多模态Faster R-CNN模型，与使用F1得分的先前工作相比，该模型实现了最先进的结果，该结果考虑了从到提高的精确度和召回率性能，用于检测甜椒。除了提高准确性外，这种方法还可以更快地部署新水果，因为它需要边界框注释而不是像素级注释（注释边界框大约快一个数量级）。该模型被重新训练以执行七种水果的检测，整个过程需要四个小时来注释和训练每个水果的新模型。
This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from to for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.
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影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
影响因子：
19.5
作者：
Everingham, Mark;Eslami, S. M. Ali;Zisserman, Andrew
通讯作者：
Zisserman, Andrew
影响因子：
9.2
作者：
Lenz, Ian;Lee, Honglak;Saxena, Ashutosh
通讯作者：
Saxena, Ashutosh
影响因子：
8.3
作者：
Nuske, Stephen;Wilshusen, Kyle;Singh, Sanjiv
通讯作者：
Singh, Sanjiv
影响因子：
5.1
作者：
Song, Y.;Glasbey, C. A.;van der Heijden, G. W. A. M.
通讯作者：
van der Heijden, G. W. A. M.