An Improved ResNet Algorithm Based On CBAM

Flower recognition has important application value in the field of flower cultivation and planting. As a kind of fine-grained image recognition, the traditional flower recognition has the problem of low recognition accuracy. In order to solve these problems, this paper proposes a neural network algorithm of ResNet structure that integrates CBAM mechanism, and adds residual blocks of attention modules to the second layer to the fifth layer of the ResNet structure. Finally, the results are output through adaptive average pooling and full connection layer. Experimental results show that the recognition accuracy of the model is close to 98% even when the recognition is difficult and there are few training data. Compared with the traditional deep learning model, the model proposed in this paper significantly improves the recognition accuracy.