Galaxy recognition based on improved efficientNetV2S
Xingchen Yan · Applied and Computational Engineering · 2023
In order to identify effective metrics that can accurately duplicate the probability distributions resulting from human classifications, this paper analyzes an improved approach for galaxy morphologies classification. At the present stage, this field still faces the problem of insufficient quality and quantity of image data, low accuracy of computer recognition and weak generalization ability of the model. From the previous research, Convolution Neural Network (CNN) can be a valid technique to complete this task but usually spends a large time and space complexity. For the purpose of increasing effectiveness, this paper improves EfficientNetV2S to construct recognition models and characterize their performance in galaxy recognition. The procedure includes data preparation and augmentation, model structure creation, attention mechanism addition, fine-tuning, and result visualization. A Fused mobile inverted bottleneck convolution (Fuse-MBConv) structure was used to accelerate the model's convergence speed. Besides, the Convolutional block attention module (CBAM) was used to improve performance and feature representation capabilities. The model in this study can minimize complexity with the number of parameters and utilize less memory while maintaining excellent accuracy. This research is conducted on the Galaxy10 DECals dataset. Experimental results show that it achieves an 87% high precision with 20.6m parameters which is more efficient than models currently used in previous research.