Improved Data Augmentation of Deep Convolutional Neural Network for Pollen Grains Classification

Traditionally, it is a time-consuming work for experts to accomplish pollen grains classification. With the popularity of deep Convolutional Neural Network (CNN) in computer vision, many automatic pollen grains classification methods based on CNN have been proposed in recent years. However, The CNN they used often focus on the most proniment area in the center of pollen grains and neglect the less discriminative local features in the surrounding of pollen grains. In order to alleviate this situation, we propose two data augmentation operations. Our experiment results on Pollen13K achieve a weighted F1 score of 97.26% and an accuracy of 97.29%.