Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System
Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System
复制标题
基于DCNN的商业溯源系统水果自动识别
DOI：
10.5121/ijcsa.2018.8301
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Zhu
中科院分区：
文献类型：
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作者：
I. Hussain;Qian;Zhu
由于各种水果之间的相似性和外部环境变化（如光照），利用机器视觉自动识别水果被认为是一项具有挑战性的任务。提出了一种基于深度卷积神经网络（DCNN）的水果识别算法。以往的方法大多是在有限的数据集下进行检验和评价，而且没有考虑外部环境的变化，因此存在一定的局限性。在本文中的另一个主要贡献是，我们建立了水果图像数据库，有15个不同的类别，包括44406图像收集在6个月内，鉴于现有的数据集在不同的现实世界条件下的限制。图像直接作为DCNN的输入进行训练和识别，而不提取特征，并且DCNN通过自适应过程从图像中学习最佳特征。最后的决策完全基于所有区域分类的融合，使用概率机制。实验结果表明，该方法能够有效地实现水果的自动识别，准确率达到99%，能够满足真实的应用需求。
Automatically fruit recognition by using machine vision is considered as challenging task due to similarities between various types of fruits and external environmental changes e-g lighting. In this paper, fruit recognition algorithm based on Deep Convolution Neural Network(DCNN) is proposed. Most of the previous techniques have some limitations because they were examined and evaluated under limited dataset, furthermore they have not considered external environmental changes. Another major contribution in this paper is that we established fruit images database having 15 different categories comprising of 44406 images which were collected within a period of 6 months by keeping in view the limitations of existing dataset under different real-world conditions. Images were directly used as input to DCNN for training and recognition without extracting features, besides this DCNN learn optimal features from images through adaptation process. The final decision was totally based on a fusion of all regional classification using probability mechanism. Experimental results exhibit that the proposed approach have efficient capability of automatically recognizing the fruit with a high accuracy of 99% and it can also effectively meet real world application requirements.