Semantic Segmentation Algorithm of Rice Small Target Based on Deep Learning

At present, rice is generally in a state of dense adhesion and small granular volume during processing, resulting in no effective semantic segmentation method for rice to extract complete rice. Aiming at the above problems, this paper designs a small object semantic segmentation network model based on multi-view feature fusion. The overall structure of the network is divided into a multi-view feature extraction module, a super-resolution feature building module and a semantic segmentation module. The extraction ability of small target features is improved by super-resolution construction of small target detail features, and the learning ability of the network for small target features is enhanced and expanded through multi-view. At the same time, a dataset of quality inspection during rice processing was constructed. We train and test the model on this dataset. The results show that the average segmentation accuracy of the semantic segmentation model in this paper reaches 87.89%. Compared with the semantic segmentation models such as SegNet, CBAM, RefineNet, DeepLabv3+ and G-FRNet, it has obvious advantages in various indicators, which can provide rice quality detection and an efficient method of rice grain extraction.