基于双流卷积神经网络的稻米缺陷分割

Rice Defect Segmentation Based on Dual-stream Convolutional Neural Networks

目前水稻质量精细化评估因为没有水稻缺陷精细化检测相关工作而无法实现,传统的水稻质量评估都是基于粗略的缺陷有无分类而实现的。针对水稻缺陷像素级分类问题,提出了一种基于深度学习的水稻缺陷分割模型,该模型使用了一个改进的DoubleU-Net网络作为主要架构,分为NETWORK1和NETWORK2两部分,其中NETWORK1是基于VGG-19修改的U型网络结构,而NETWORK2是基于Swin Transformer修改的U型网络结构,将这两部分串联起来,同时融合CNN局部信息提取和Transformer全局信息提取的优势,可以更好地捕捉图像的上下文信息。同时,使用了多重损失函数,包括加权的二元交叉熵损失、加权的交并比损失和一个无需训练的智能损失网络,在提高模型训练稳定性的同时进一步提高了模型分割的精度。在制作的密集水稻缺陷数据集上进行训练测试,该模型均取得了较其他方法更好的分割性能,具有鲁棒性和较好的泛化能力。

Currently,fine-grained assessment of rice quality cannot be achieved due to the lack of related work on fine-grained detection of rice defects.Traditional rice quality assessment is based on rough classification of defect presence or absence.To address 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 information 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.