Rice Defect Segmentation Based on Dual-stream Convolutional Neural Networks
Currently,fine-grained assessment of rice quality cannot be achieved due to the lack of related work on fine-grained de-tection of rice defects.Traditional rice quality assessment is based on rough classification of defect presence or absence.To ad-dress the problem of pixel-level classification of rice defects,a deep learning-based rice defect segmentation model is proposed.The model uses an improved DoubleU-Net network as the main architecture,which consists of two parts,NETWORK1 and NETWORK2.NETWORK1 is based on a modified U-shaped network structure of VGG-19,while NETWORK2 is based on a modified U-shaped network structure of Swin Transformer.The two parts are concatenated,and the advantages of CNN local in-formation extraction and Transformer global information extraction are integrated to better capture the contextual information of images.In addition,multiple loss functions are used,including weighted binary cross-entropy loss,weighted intersection-over-union loss,and an intelligent loss network that does not require training,to improve the stability of the model training and further improve the accuracy of model segmentation.The proposed model is trained and tested on a densely annotated rice defect dataset,and achieves better segmentation performance than other methods,with robustness and good generalization ability.
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