Deep Residual SENet for Foliage Recognition
Foliage morphological features are important for plant recognition. However, the foliage shape generally presents big intra class variations and small inter class differences. This brings a great challenge to accurate plant foliage recognition. In this paper, we propose a deep residual squeeze excitation network (R SENet) for foliage recognition. Firstly, R SENet learns and obtains the significance levels of each channel of the various convolutional layers in a residual block to recognition tasks via squeeze excitation strategy. Then, the weights of each channel are rescaled by means of the significances to promote the relevant channels and inhibit non important channels. Finally, we evaluate the proposed approach on the well known Flavia dataset for foliage recognition. The experimental results indicate that our approach achieves more accurate average recognition rate (up to 97.86% and more robustness to noise than other outstanding approaches.
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