基于改进甲壳虫全域搜索算法的机织物疵点检测

Fabric defect detection based on improved cross-scene Beetle global search algorithm

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 image was higher.To verify whether the proposed method extracted the features of defects,the Grad CAM method was used to visualize the defect features extracted by the model.The focus of this method was more concentrated within the range of fabric defects,and it was less affected by the background area.This verified that this method could effectively identify defect areas and has good generalization performance for cross-scene fabric defect detection.The optimal Gabor filter was used for fabric defect detection,and the defect detection effect of the proposed algorithm was evaluated in the form of binary images.The results show that the fabric defects detected by this algorithm are clear and accurate.Conclusion The loss function curve of the method in this paper converge quickly,and the accuracy curve convergence value is high.The space for extracting all defect features of defects is closer,and the distance between each defect is larger.This proves that the features extracted from fabric defects in this paper are classified differently,and are less affected by the background.It verifies that the method in this paper can effectively identify the defect area with good generalization performance for cross-scene fabric defect detection.In practical applications,obtaining fabric images is influenced by lighting and fabric texture,which can affect the applicability of the model across-scenes,such as camera angle,fabric type,and camera parameters.The next step is to conduct research on other influencing factors such as camera angle and fabric type to further improve the model′s generalization ability across scenes.