基于深度卷积神经网络的番茄主要器官分类识别方法

发布时间：2018-05-05 18:29

本文选题：目标识别 + 图像处理　；参考：《农业工程学报》2017年15期

【摘要】：为实现番茄不同器官的快速、准确检测,提出一种基于深度卷积神经网络的番茄主要器官分类识别方法。在VGGNet基础上,通过结构优化调整,构建了10种番茄器官分类网络模型,在番茄器官图像数据集上,应用多种数据增广技术对网络进行训练,测试结果表明各网络的分类错误率均低于6.392%。综合考虑分类性能和速度,优选出一种8层网络用于番茄主要器官特征提取与表达。用筛选出的8层网络作为基本结构,设计了一种番茄主要器官检测器,结合Selective Search算法生成番茄器官候选检测区域。通过对番茄植株图像进行检测识别,试验结果表明,该检测器对果、花、茎的检测平均精度分别为81.64%、84.48%和53.94%,能够同时对不同成熟度的果和不同花龄的花进行有效识别,且在检测速度和精度上优于R-CNN和Fast R-CNN。
[Abstract]:In order to detect tomato organs quickly and accurately, a classification and recognition method of tomato main organs based on deep convolution neural network was proposed. On the basis of VGGNet, 10 kinds of tomato organ classification network models were constructed by optimizing and adjusting the structure. The network was trained by using a variety of data augmentation techniques on the tomato organ image data set. The test results show that the classification error rate of each network is lower than that of 6.392. Considering the classification performance and speed, an 8-layer network was selected for feature extraction and expression of major organs of tomato. Using the selected 8-layer network as the basic structure, a tomato main organ detector is designed, and the candidate detection region of tomato organ is generated with Selective Search algorithm. The results showed that the detection accuracy of the detector was 81.64%, 84.48% and 53.94%, respectively, which could be used to identify the fruit of different maturity and the flower of different flower age at the same time, the results showed that the detection accuracy of the detector was 81.64% and 53.94g% respectively, and the results showed that the detection accuracy of the detector was 81.64% and 53.94%, respectively. It is superior to R-CNN and Fast R-CNN in detecting speed and accuracy.
【作者单位】：沈阳农业大学信息与电气工程学院;
【基金】：辽宁省科学事业公益研究基金(2016004001) 国家自然科学基金(31601218)
【分类号】：S641.2;TP391.41

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