Rice disease identification method based on improved Mask R-CNN

Abstract

In the complex and changeable actual scene, the identification of rice diseases generally has the problem of small and dense targets. The current method mainly introduces the spatial regularization loss into the Fast R-CNN framework, and integrates the detection and technical tasks into a model. Due to the limitations of this method in the actual scene, a rice disease recognition method based on improved Mask R-CNN is proposed in this paper. This proposed method mainly improves the feature fusion process of the feature pyramid, changes the original top-down path to bottom-up to retain more spatial position information in the shallow feature map, and adds multi-scale dilation convolution in the feature fusion process to increase the receptive field of the feature map and keep the resolution unchanged, which can avoid the loss of features in the down-sampling process; the dataset was established and labeled by using the photographs of rice bacterial blight. The proposed algorithm is compared with the Mask R-CNN algorithm. When the IOU is 0.6, 0.7 and 0.8, the accuracy is increased by 0.46, 0.66 and 0.48 respectively. Experimental results show that the algorithm can get more accurate results on the established dataset.