Wild Mushroom Recognition Based on Attention Mechanism and Feature Pyramid

Abstract

In order to reduce the occurrence of wild mushroom poisoning incidents, and at the same time reduce the impact of the complex background of wild mushroom pictures on the recognition accuracy, this paper uses the Squeeze-and-Excitation attention mechanism and feature pyramid to improve the ResNet50 network. First, in order to increase the correlation between channels, the Squeeze-and-Excitation attention mechanism is added to the residual block of the ResNet50 network. Second, the feature pyramid is used to fuse the features between different layers of the network. Next, send the lowest feature map which fused by FPN to the fully connected layer. At last, the final result is normalized by softmax function and classified. The experimental results show that the accuracy of the method can reach 95.97%, which is 2.71% higher than the unimproved ResNet50 network. The comparison results show that it is better than the three network models of VGG19, DenseNet161 and Iception_v3, the accuracy rates are increased by 6.40%, 6.31% and 2.28% respectively.