摘要
为解决深度学习模型在面对跨场景的织物疵点检测时存在泛化性能差的问题,在甲壳虫全域搜索算法(BAS)的基础上添加了本地搜索能力构建了一种基于甲壳虫算法的混合算法,该算法可具体分为训练阶段和检测阶段。在训练阶段,通过对无疵点织物进行训练构建二维Gabor滤波器,然后使用改进BAS的混合模型对Gabor滤波器的参数进行了优化,使改进后的算法具备全局搜索和局部搜索的能力;在检测阶段,根据在训练阶段获得最佳参数构造Gabor滤波器,对待检测的织物图像进行卷积运算,并对卷积后图像进行二值化处理,最终识别待测试织物是否含有疵点。实验结果表明:该方法的特征提取具有良好的类别区分性,更加集中在疵点范围内,检测准确率可达99.26%,具有良好的稳定性和泛化性能。
Objective Deep learning models have poor generalization performance when faced with cross-scene fabric defect detection,and there is relatively little research on dynamic cross scene transfer methods.Due to the influence of camera type,parameters,environmental lighting and other image acquisition conditions,there are significant differences in the distribution of fabric image data.How to accurately extract target domain data under various imaging conditions and achieve effective detection of fabric defects across scenes is an urgent problem to be solved.To this end,a hybrid algorithm based on the Beetle global search algorithm(BAS)was constructed by adding local search capabilities to the global search capability of BAS to tackle the complexity and diversity in fabric images during fabric defect detection.Method This research constructed a hybrid algorithm based on the Beetle algorithm by adding local search capabilities to the global search capability of BAS.Gabor filters were used to select the optimal parameters and establish a fabric detection scheme.In order to solve the optimization problem of the Beetle algorithm,local search capability was added to the global search capability of BAS.In order to obtain accurate binarization detection results,the image underwent threshold segmentation based on the use of low-pass filtering to convolution the results again.Results In order to verify the effectiveness of the BAS model,the method proposed in this paper was compared with the methods in references.It was seen that the accuracy curve of this method was improved fastest and took the shortest time to reach the maximum accuracy,but the loss function curve fluctuated greatly.To verify the accuracy of the proposed method,the T-SNE method was used to visualize the features of fabric defects using the improved BAS method and the methods used in references.The method in this article showed a smaller distance in the embedding space,but the feature similarity extracted from defects and hole defects at the edge of the imag
作者
李杨
张永超
彭来湖
胡旭东
袁嫣红
LI Yang;ZHANG Yongchao;PENG Laihu;HU Xudong;YUAN Yanhong(School of Automation,Zhejiang Mechanical and Electrical Vocational and Technical College,Hangzhou,Zhejiang 310000,China;Key Laboratory of Modern Textile Machinery&Technology of Zhejiang Province,Zhejiang Sci-Tech University,Hangzhou,Zhejiang 310018,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2024年第10期89-94,共6页
Journal of Textile Research
基金
浙江省“尖兵”计划项目(2022C0065)。
关键词
深度学习
全域搜索算法
GABOR滤波器
织物疵点检测
泛化性能
图像识别
deep learning
global search algorithm
Gabor filter
fabric defect detection
generalization performance
pattern recognition
