摘要
为了提升原棉混合杂质图像的分割准确率和时效性,提出一种改进UNet模型的原棉杂质分割算法。在ResNet50结构基础上设计新的编码模块,采用1个卷积层将最后3个残差模块和全连接层进行替换,对模型参数量实施优化,提高图像特征信息的提取能力;设计CEloss与Dice loss组合的损失函数,优化正负样本比例不均衡,改善模型细小杂质的分割能力;最后,运用迁移学习方法,以VOC数据集为基础,对主干网络权重初始化编码器预训练,进一步优化因图像数据量少导致的模型收敛慢等问题。实验验证表明,改进方法MPA值提升24.52%,准确率达到88.24%,单张图像处理时间缩短至21.9 ms,较传统方法提升49.42%,此改进方法可有效提升原棉杂质图像分割的准确率和时效性。
To improve the segmentation accuracy and timeliness of raw cotton mixed impurity images,a new improved UNet model of raw cotton impurity segmentation algorithm was proposed.A new coding module was designed based on the ResNet50 structure,and one convolutional layer was used to replace the last three residual modules and the fully connected layer to implement optimization of the model parametric quantities and improve the extraction of image feature information.The loss function of CEloss and Dice loss was designed to optimize the imbalance between positive and negative samples and improve the segmentation ability of the model for fine impurities.Finally,the migration learning method was applied to pre-train the encoder for the initialization of the backbone network weights based on the VOC dataset to further optimize the slow convergence of the model due to the small amount of image data.The experimental validation shows that the MPA value of the method is improved by 24.52%,the accuracy rate reaches 88.24%,and the processing time of a single image is shortened to 21.9 ms,which is 49.42%higher than that of the traditional method.The improved method described is effective in improving the accuracy and timeliness of image segmentation of raw cotton impurities.
作者
许涛
麻爱松
吕欢
郭强
高琛
XU Tao;MA Aisong;LYU Huan;GUO Qiang;GAO Chen(School of Mechanical and Electrical Engineering,Xi’an Polytechnic University,Xi’an 710600,China;Xi’an Key Laboratory of Modern Intelligent Textile Equipment,Xi’an Polytechnic University,Xi’an 710600,China)
出处
《西安工程大学学报》
CAS
2023年第1期77-83,共7页
Journal of Xi’an Polytechnic University
基金
陕西省自然科学基金(2019JM310)
西安市现代智能纺织装备重点实验室资助项目(2019220614SY021CG043)。
关键词
残差网络
UNet
迁移学习
原棉杂质
图像分割
residual network
UNet
transfer learning
raw cotton impurities
image segmentation
