Rapid Fine-Grained Classification of Butterflies Based on FCM-KM and Mask R-CNN Fusion

Abstract

Butterfly recognition is a key link in the field of animal and plant observation. In order to realize the location and recognition of butterflies by robot vision system in complex environment, rapid fine-grained classification of butterflies based on the FCM-KM and Mask R-CNN fusion was proposed. First, an adaptive image enhancement algorithm based on fuzzy sets optimized by FOA was used to realize the adaptive fuzzy enhancement of butterfly images in image pre-processing. Then, K-Means clustering algorithm optimized by dynamic population firefly algorithm based on chaos theory and max-min distance algorithm, FCM-KM, was used to determine the optimal clustering number K instead of manual parameter tuning. Finally, while effectively segmenting the butterfly images, the Softmax in Mask R-CNN was used to classify the butterfly images. The recognition accuracy of the trained model in the verification set was 83.62%. To verify the feasibility and effectiveness of the model in complex environment, the rapid fine-grained classification method of butterflies based on the FCM-KM and Mask R-CNN fusion was compared with CNN, Resnet and original Mask R-CNN. The experiment results show that the method proposed in this paper has a good classification effect on butterflies in complex environment.