Study on Image-based Classification of Honeycomb Cells Using Feature-fused SSD

Abstract

The purpose of this study is developing a technology to support honeybee management in the beekeeping industry. We have been studying a method for detecting cells and classifying their states (larva, pupa, pollen, etc.) from honeycomb images using SSD. However, there were some problems, such as, misclassification of cells having similar features and detection miss of smaller larva. To solve these problems, we used Feature fused SSD (FSSD) that is able to use attributes of small objects by combining feature maps in our system. We evaluated its performance for the honeycomb cell classification task in this paper.