Insect Recognition Under Natural Scenes Using R-FCN with Anchor Boxes Estimation.

Insect species recognition is an important application of computer vision in zoology and agriculture. Most of existing methods resort to hand-crafted features and traditional classifiers, which usually give poor accuracy and apply only to elaborately taken full-size pictures. In this paper, we focus on a more challenging case where the images are taken in the wild with complex backgrounds, and propose to use a deep learning based detection model to deal with it. It exploits multi-class object detection to eliminate interferences from complex backgrounds, while taking advantages of deep learning to significantly improve the performance of recognition. After evaluating several popular detection methods, R-FCN is selected as the base model. To further improve its performance, we introduce a clustering algorithm for estimation of the anchor boxes instead of using predefined ones. The experimental results on a dataset of insect images collected in the wild prove the effectiveness of our proposed method in improving both accuracy and speed.