关键词: 图像分割, 水稻, 密度检测, DeepLabV3+

Abstract: Realtime acquisition of grain growth density is helpful to reduce the lag in the detection process of combine harvester feeding amount. In order to obtain grain growth density efficiently and in real time, this paper proposes a realtime detection method of rice growth density based on improved DeepLabV3+ semantic segmentation model. In order to reduce the influence of convolution on feature fusion in the decoder, this study adds the Shuffle Attention module after the convolution module of the encoder part and the backbone network, which reduces the semantic loss. By improving DeepLabV3+, the rice image is semantically segmented and the proportion of rice pixels in the whole image is calculated, and the rice growth density detection model is obtained by fitting with the actual measured rice growth density. The results showed that there was a strong linear relationship between rice pixel ratio and rice growth density per unit area, the determination coefficient R2 was 0.68, and the root mean square error RMSE of the test set was 0.46kg/m2, which could meet the needs of field rice growth density detection.

Key words: image segmentation, rice, density detection, DeepLabV3+