论文标题
轰炸物种图像分类
Bombus Species Image Classification
论文作者
论文摘要
昆虫学家，生态学家和其他人都难以快速，准确地确定他们在现场工作和研究中遇到的大黄蜂种类。当前的过程要求将蜜蜂安装，然后实际运送给分类专家进行适当的分类。我们研究了转移学习中得出的图像分类系统是否可以完成此任务。我们使用了Google Inception，Oxford VGG16和VGG19和Microsoft Resnet 50。我们发现Inception和VGG分类器能够从可用的数据中识别蜂物物种方面取得了一些进展，而Resnet却没有。单个分类器的单个物种识别和44％的前3个标签的精度最高为23％，其中复合模型的性能更好，27％和50％。我们认为，通过有限的5,000多种图像标记的图像的有限数据集最大程度地阻碍了性能，单个物种以59 -315张图像为代表。
Entomologists, ecologists and others struggle to rapidly and accurately identify the species of bumble bees they encounter in their field work and research. The current process requires the bees to be mounted, then physically shipped to a taxonomic expert for proper categorization. We investigated whether an image classification system derived from transfer learning can do this task. We used Google Inception, Oxford VGG16 and VGG19 and Microsoft ResNet 50. We found Inception and VGG classifiers were able to make some progress at identifying bumble bee species from the available data, whereas ResNet was not. Individual classifiers achieved accuracies of up to 23% for single species identification and 44% top-3 labels, where a composite model performed better, 27% and 50%. We feel the performance was most hampered by our limited data set of 5,000-plus labeled images of 29 species, with individual species represented by 59 -315 images.
