ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (8): 1609-1618.doi: 10.7544/issn1000-1239.2018.20180181

所属专题: 2018数据挖掘前沿进展专题

• 人工智能 • 上一篇    下一篇

蝴蝶种类自动识别研究

谢娟英1,侯琦1,史颖欢2,吕鹏3,景丽萍4,庄福振5,张军平6,谭晓阳7,许升全8   

  1. 1(陕西师范大学计算机科学学院 西安 710119);2(南京大学计算机科学与技术系 南京 210023);3(山东财经大学计算机科学与技术学院 济南 250014);4(北京交通大学计算机与信息技术学院 北京 100044);5(中国科学院计算技术研究所 北京 100190);6(复旦大学计算机科学技术学院 上海 200433);7(南京航空航天大学计算机科学与技术学院 南京 210016);8(陕西师范大学生命科学学院 西安 710119) (xiejuany@snnu.edu.cn)
  • 出版日期: 2018-08-01
  • 基金资助: 
    国家自然科学基金项目(61673251);中央高校基本科研业务费专项资金项目(GK201701006) This work was supported by the National Natural Science Foundation of China (61673251) and the Fundamental Research Funds for the Central Universities (GK201701006).

The Automatic Identification of Butterfly Species

Xie Juanying1, Hou Qi1, Shi Yinghuan2, Lü Peng3, Jing Liping4, Zhuang Fuzhen5, Zhang Junping6, Tan Xiaoyang7,Xu Shengquan8   

  1. 1(School of Computer Science, Shaanxi Normal University, Xi’an 710119);2(Department of Computer Science & Technology, Nanjing University, Nanjing 210023);3(School of Computer Science & Technology, Shandong University of Finance and Economics, Jinan 250014);4(School of Computer & Information Technology, Beijing Jiaotong University, Beijing 100044);5(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190);6(School of Computer Science, Fudan University, Shanghai 200433);7(College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016);8(College of Life Sciences, Shaanxi Normal University, Xi’an 710119)
  • Online: 2018-08-01

摘要: 针对现有蝴蝶识别研究中所用数据集蝴蝶种类偏少,且只含有蝴蝶标本照片、不含生态环境中蝴蝶照片的问题,发布了一个同时包含标本照片和生态照片的蝴蝶图像数据集,其中标本照片包含全部中国蝶类志蝴蝶种类,共计4270张照片、1176种,蝴蝶生态环境下照片1425张、111种.提出基于深度学习技术Faster R-CNN的蝴蝶种类自动识别系统,包括生态照片中蝴蝶位置的自动检测和物种鉴定.实验去除只含有单张生态照片的蝴蝶种类,对剩余的蝴蝶生态照片进行5-5划分,构造2种不同训练数据集:一半生态照片+全部模式照片、一半生态照片+对应种类模式照片;训练3种不同网络结构的蝴蝶自动识别系统,以平均精度均值(mean average precision, mAP)为评价指标,采用上下、左右翻转、不同角度旋转、加噪、不同程度模糊、对比度升降等9种方式扩充训练集.实验结果表明,基于Faster R-CNN深度学习框架的蝴蝶自动识别系统对生态环境中的蝴蝶照片能实现其中蝴蝶位置的自动检测和物种识别,模型的mAP最低值接近60%,并能同时检测出生态照中的多只蝴蝶和完成物种识别.

关键词: 蝴蝶, 自动识别, 目标检测, 深度学习, 分类

Abstract: The available butterfly image data sets comprise a few limited species, and the images in the data sets are always standard patterns without the images of butterflies in their living environments. To overcome the aforementioned limitations in the butterfly image data sets, we build a butterfly image data set composed of all species of butterflies in Monograph of Chinese butterflies with 4270 standard pattern images of 1176 butterfly species, and 1425 butterfly images from living environment of 111 species. We use the deep learning technique Faster R-CNN to develop an automatic butterfly identification system including butterfly position detection in images from living environment and species recognition. We delete those butterfly species with only one living environment image from data set, then partition the rest butterfly images from living environment into two subsets in half-half partition way, such that one is used as testing subset, and the other is respectively combined with all standard patterns of butterfly images or the standard patterns of butterfly images with the same species as the images from living environment to get two different training subsets. In order to construct the training subset for Faster R-CNN, nine methods are adopted to amplify the images in the training subset including the turning of up and down, and left and right, rotation with different angles, adding noises, blurring, and contrast ratio adjusting etc. Three kinds of network structure based prediction models are trained. The mAP (mean average prediction) criterion is used to evaluate the performance of the predictive models. The experimental results demonstrate that our Faster R-CNN based butterfly automatic identification system performs well. Its worst mAP is up to 60%, and it can simultaneously detect the positions of more than one butterflies in one image from living environment and can recognize their species as well.

Key words: butterflies, automatic identification, object detection, deep learning, classification

中图分类号: