| 自然环境下多类水果采摘目标识别的通用改进SSD模型 |
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| 引用本文： | 彭红星,黄博,邵园园,李泽森,张朝武,陈燕,熊俊涛.自然环境下多类水果采摘目标识别的通用改进SSD模型[J].农业工程学报,2018,34(16):155-162. |
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| 作者姓名： | 彭红星 黄博 邵园园 李泽森 张朝武 陈燕 熊俊涛 |
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| 作者单位： | 华南农业大学数学与信息学院/南方农业机械与装备关键技术教育部重点实验室;山东农业大学机械与电子工程学院;华南农业大学工程学院 |
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| 基金项目： | 国家自然科学基金项目（51578162，31571568，31701325）；广东省自然科学基金项目（2015A030310258，2018A030313330）；山东省重点研发计划（2016GNC112007）；广东省科技计划项目（2015A020209111）；广州市科技计划项目（201802020032） |
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| 摘 要： | 为解决当前自然环境下水果识别率不高、泛化性不强等问题,该文以苹果、荔枝、脐橙、皇帝柑4种水果为研究对象,提出了一种改进的SSD(single shot multi-boxdetector)深度学习水果检测模型:将经典SSD深度学习模型中的VGG16输入模型替换为Res Net-101模型,并运用迁移学习方法和随机梯度下降算法优化SSD深度学习模型。该文基于Caffe深度学习框架,对自然环境下采集的水果图像进行不同网络模型、不同数据集大小和不同遮挡比例等多组水果识别检测效果对比试验。试验表明:改进的SSD深度学习水果检测模型对4种水果在各种环境下的平均检测精度达到88.4%,高于经典SSD深度学习模型中的86.38%,经过数据增强后平均检测精度可提升至89.53%,在遮挡面积低于50%的情况下F1值能达到96.12%,有较好的泛化性和鲁棒性,可以很好地实现自然环境下多类水果的精准检测,可为农业自动化采摘中的水果识别检测问题提供新的方案。
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| 关 键 词： | 图像识别 模型 算法 水果检测 深度学习 SSD VGG16 ResNet-101 |
| 收稿时间： | 2018/2/26 0:00:00 |
| 修稿时间： | 2018/4/29 0:00:00 |
General improved SSD model for picking object recognition of multiple fruits in natural environment |
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| Peng Hongxing,Huang Bo,Shao Yuanyuan,Li Zesen,Zhang Chaowu,Chen Yan and Xiong Juntao.General improved SSD model for picking object recognition of multiple fruits in natural environment[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(16):155-162. |
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| Authors: | Peng Hongxing Huang Bo Shao Yuanyuan Li Zesen Zhang Chaowu Chen Yan and Xiong Juntao |
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| Institution: | 1. College of Mathematics and Informatics, South China Agricultural University / Key Laboratory of Key Technology for South Agricultural Machine and Equipment, Ministry of Education, Guangzhou 510642, China;,1. College of Mathematics and Informatics, South China Agricultural University / Key Laboratory of Key Technology for South Agricultural Machine and Equipment, Ministry of Education, Guangzhou 510642, China;,2. School of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai''an 271018, China;,1. College of Mathematics and Informatics, South China Agricultural University / Key Laboratory of Key Technology for South Agricultural Machine and Equipment, Ministry of Education, Guangzhou 510642, China;,1. College of Mathematics and Informatics, South China Agricultural University / Key Laboratory of Key Technology for South Agricultural Machine and Equipment, Ministry of Education, Guangzhou 510642, China;,3. College of Engineering, South China Agricultural University, Guangzhou 510642, China and 1. College of Mathematics and Informatics, South China Agricultural University / Key Laboratory of Key Technology for South Agricultural Machine and Equipment, Ministry of Education, Guangzhou 510642, China; |
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| Abstract: | Abstract: China is the leading country in the world for the production of fruits, and the variety of fruits is very wide. But fruits harvesting heavily depends on manual hand picking, and it''s time-consuming, low efficient and labor-intensive. Fruit picking robot can realize the automation of fruit picking operation and solve the problems of shortage of labor force and high cost. Fruit identification with machine vision is the primary task. But in the field environment, fruit images are easily affected by many external environmental factors such as light changes, fruit size difference, complicated background noise, which can reduce the identification accuracy of fruit with traditional fruit recognition algorithm. And without general feature extraction model, traditional fruit recognition algorithm can only focus on one specific fruit. Deep learning algorithm has the advantages of strong non-linear feature expression ability, and good generalization performance, and can avoid the subjectivity and limitation of human selection on feature selection. In order to solve the problems of low recognition rate and weak generalization for fruit recognition in the field environment, with the apple, litchi, navel orange, Huangdi gan as the research object, an improved single shot detector (SSD) deep learning model for fruit detection is put forward in this study. That is to use ResNet-101 model to replace the VGG16 network in the classic SSD detection framework. After the replacement, the framework still uses 6 feature extraction layers to predict the type and location of fruit objects at each layer; then the weight model under the large data sets is transplanted to multi-class fruit detection tasks with the method of transfer learning. The SSD deep learning model is optimized by using SGD (stochastic gradient descent) algorithm. The weight model of the pre-training on the ImageNet data set is used as the initial weight model of the SSD detection framework, and the training time and resources are further reduced by transferring the characteristics of the learning. At the same time, data enhancement method is used to improve the robustness of the algorithm without reducing the detection accuracy. Based on the Caffe deep learning framework, fruit detection results are compared for the multi-class fruit pictures collected in the field environment with different network models, different data set sizes and different occlusion ratios. Experimental results show that after a day of training, the residual error reference model takes about 0.14 s when detecting the image with a resolution of 500×500 pixels, only about 0.09 s slower than the VGG16 network model. And in various environments, the average detection accuracy of the 4 kinds of fruit based on the improved SSD deep learning fruit detection model can reach 88.4%, which is higher than that of the classic SSD deep learning model that is 86.38%. After data are enhanced, the average detection accuracy can be improved by 1.13 percentage points and reach 89.53%, and the F1-score can reach 96.12% when the occlusion area is lower than 50%. Therefore, compared with the traditional recognition algorithm, this method based on improved SSD model can realize multi-class fruit image detection simultaneously without artificial feature selection for different fruit images, and has better generalization and robustness. It can achieve accurate detection of multiple kinds of fruits in the field environment, and provides a new solution for the problem of fruit detection and recognition in agricultural automation. |
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| Keywords: | image recognition models algorithms fruit detection deep learning SSD VGG16 ResNet-101 |
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