Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively poor.Deep learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex shapes.Therefore,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect detection.In MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects.On this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects.In this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds.The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

Review of Fabric Defect Detection Based on Computer Vision

In textile inspection field,the fabric defect refers to the destruction of the texture structure on the fabric surface.The technology of computer vision makes it possible to detect defects automatically.Firstly,the overall structure of the fabric defect detection system is introduced and some mature detection systems are studied.Then the fabric detection methods are summarized,including structural methods,statistical methods,frequency domain methods,model methods and deep learning methods.In addition,the evaluation criteria of automatic detection algorithms are discussed and the characteristics of various algorithms are analyzed.Finally,the research status of this field is discussed,and the future development trend is predicted.

Fabric Defect Detection Using Adaptive Wavelet Transform

A method of woven fabric defect detection using the wavelet transform adaptive to the fabric has been developed. With reference to the orthogonality constrains of Daubechies wavelet, by taking the mmimization of the energy or the gray level of the pixels in the output sub-images as the additional conditions and using the random algorithm method, two sets of wavelet filters adapted to the fabric texture were formed. The original images of normal fabric texture and the fabric texture with defects were decomposed into horizontal and vertical sub- images by using these filters and the feature indices of these sub-images were also extracted. By comparing the feature indices of the normal texture with that of the defective texture, the fabric defects can be successfully detected and located.

Detection of Fabric Defects with Fuzzy Label Co-occurrence Matrix Set

Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.

Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis

To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced.

Realization of Orthogonal Wavelets Adapted to Fabric Texture for Defect Detection

The wavelet adapted to the fabric texture can be developed from the orthogonal and normal series which are selected randomly by means of Monte Carlo method and op timized by adding certain constraint conditions.Then the fabric image can be decomposed into the subimages by the adaptive wavelet transform and the horizontal and vertical texture information will be perfectly contained in the subimages. Therefore this method can be effectively used for the automatic inspection of the fabric defects.

Automatic Fabric Defects Inspection Machine

The textile industry is one of the most important industries in Sri Lanka.In most of the textile garment factories the defects of the fabrics are detected manually.The manual textile quality control usually depends on eye inspection.Famously,human visual assessment is drawn-out,tiring,and an exhausting errand,including perception,consideration and experience to recognize the fault occurrence.The precision of human visual assessment declines with dull positions and vast schedules.Some of the time slow,costly,and sporadic review is the outcome.In this manner,the programmed automatic visual review safeguards both the fabric quality inspector and the quality.This examination has exhibited that Textile Defect Recognition System is fit for distinguishing fabrics’imperfections with endorsed exactness with viability.With some products 100%inspection is important to ensure the stipulated quality or standard.The classifications for the automated fabric inspection approaches are expanding as the work is vast and complex.According to the algorithm used,the texture analysis problem is classified into different approaches.They are Structural,spectral,model-based methods,Unfortunately,the optimal plan does not yet exist for these vast numbers of applied methods,as each of them has some advantages and disadvantages.

Automatic Image Inspection of Fabric Defects Based on Optimal Gabor Filter

An effective method for automatic image inspection of fabric defects is presented. The proposed method relies on a tuned 2D-Gabor filter and quantum-behaved particle swarm optimization( QPSO) algorithm. The proposed method consists of two main steps:( 1) training and( 2) image inspection. In the image training process,the parameters of the 2D-Gabor filters can be tuned by QPSO algorithm to match with the texture features of a defect-free template. In the inspection process, each sample image under inspection is convoluted with the selected optimized Gabor filter.Then a simple thresholding scheme is applied to generating a binary segmented result. The performance of the proposed scheme is evaluated by using a standard fabric defects database from Cotton Incorporated. Good experimental results demonstrate the efficiency of proposed method. To further evaluate the performance of the proposed method,a real time test is performed based on an on-line defect detection system. The real time test results further demonstrate the effectiveness, stability and robustness of the proposed method,which is suitable for industrial production.

An Enhanced Nonlocal Self-Similarity Technique for Fabric Defect Detection

Fabric defect detection has been an indispensable and important link in fabric production,many studies on the development of vision based automated inspection techniques have been reported.The main drawback of existing methods is that they can only inspect a particular type of fabric pattern in controlled environment.Recently,nonlocal self-similarity(NSS)based method is used for fabric defect detection.This method achieves good defect detection performance for small defects with uneven illumination,the disadvantage of NNS based method is poor for detecting linear defects.Based on this reason,we improve NSS based defect detection method by introducing a gray density function,namely an enhanced NSS(ENSS)based defect detection method.Meanwhile,mean filter is applied to smooth images and suppress noise.Experimental results prove the validity and feasibility of the proposed NLRA algorithm.

Fabric Defect Detection Using GMRF Model

It has been testified that the Gauss Markov random field model is most suitable for the characterization of fabric texture among a variety of available models because of its approximately constant character and the normality of the gray-level distribution found with typical fabric images. However, the general Gauss-Markov random field(GMRF) method for fabric defect detection is not always ideal in practice since in some cases, the estimated model parameters make the Markov error covariance not positively definite, which may render the method to fail thoroughly. In this paper, the use of the GMRF model for defect detection of fabric is discussed and an approach to this problem is proposed. Some detailed texture may be overlooked in this way, but good detection results can still be expected as far as fabric defect detection is concerned.

Detection of fabric defects based on frequency-tuned salient algorithm

The correct rate of detection for fabric defect is affected by low contrast of images. Aiming at the problem,frequencytuned salient map is used to detect the fabric defect. Firstly,the images of fabric defect are divided into blocks. Then,the blocks are highlighted by frequency-tuned salient algorithm. Simultaneously,gray-level co-occurrence matrix is used to extract the characteristic value of each rectangular patch. Finally,PNN is used to detect the defect on the fabric image. The performance of proposed algorithm is estimated off-line by two sets of fabric defect images. The theoretical argument is supported by experimental results.

基于轻量化YOLOv7的织物疵点检测算法研究

由于检测工艺的不完善和外界因素的影响,织物疵点检测过程中会存在目标漏检和误检的情况,并且为了在移动设备和嵌入式设备中部署,提出了一种基于轻量化YOLOv7的织物疵点检测算法(LFD-YOLOv7)。首先,针对YOLOv7算法网络结构复杂和参数量较大的问题,结合GhostNet网络构建EGM模块来取代主干网络中的ELAN模块,降低了网络的复杂度和计算瓶颈,增强网络的学习能力;其次,基于ShuffleNetv2的思想,将其与残差网络相融合构造了S-SPPCSPC模块,使网络更加轻量化;然后,引入CA注意力机制来抑制背景噪声对目标检测的影响,提高小目标的准确率;最后采用SIoU损失函数来优化输出预测框边界,提高算法收敛速度。试验结果表明:与YOLOv7算法相比,LFD-YOLOv7算法平均检测精度提升了5.59个百分点,参数量减少了30.3%,检测速度达到41帧/s,满足纺织工业生产对织物疵点的准确性和实时性要求。

改进YOLOv5的织物缺陷检测方法

为了在不增加网络参数量的条件下提升深度学习方法对织物缺陷检测的精度,提出了一种基于改进YOLOv5的织物缺陷检测方法。通过深度卷积改造通道注意力,剪裁最大池化优化空间注意力,并通过二者构建的双级联注意力机制来搭建特征提取子网络,从而提高网络对缺陷区域纹理和语义特征的提取能力;采用鬼影混洗卷积改进特征融合子网络,强化对提取特征的筛选,在降低模型参数量的同时,改善缺陷信息丢失和无效信息冗余问题;在检测端引入具有角度损失的新型损失函数SIOU,来促进真实框和预测框的拟合并提升对缺陷预测的准确性。实验结果表明:改进的YOLOv5方法在降低YOLOv5基准模型复杂度和计算量的同时,与YOLOv7等六种先进方法相比,可获得更高的检测精度,相较原模型mAP@0.5值提高了2.6个百分点,mAP@0.5:0.9值提高了1.3个百分点。

基于改进YOLOv5算法的织物缺陷检测

基于现有织物缺陷检测算法受疵点尺寸与织物纹理背景的影响导致检测精度较低,同时检测模型过于复杂,难以部署到工控设备上,无法满足织物缺陷实时检测等现状,提出一种改进YOLOv5算法的织物缺陷检测算法。以YOLOv5算法为基准模型,采用跨阶段部分连接残差网络替代原模型的主干网络,增强模型上下文特征信息学习能力;将SimAM注意力机制融入到模型中,提升对有用特征的提取能力,抑制无用纹理背景特征的干扰;引入WIoU与Varifocal Loss损失函数,提高回归框准确性的同时降低负样本权重;最后,针对织物的小目标疵点难以检测的问题,提出增加小目标检测层的方法,提高模型的检测能力。试验结果表明:该研究算法能够快速准确地检测织物疵点,精确率与mAP分别达到86.46%与84.4%,与基准模型相比,分别提高6.16个百分点和5.8个百分点。

基于改进Res-UNet网络的织物瑕疵图像识别方法

复杂花色织物的纹理和色彩常常是非规则的,导致织物表面瑕疵识别难度较高。针对上述问题,研究一种基于改进Res-UNet网络的织物表面瑕疵图像识别方法。采集织物图像并对其实施灰度化、去噪以及直方图均衡化处理,利用蝙蝠算法求取最佳提取网络层数,通过增加特征提取网络层数改进Res-UNet网络,利用改进后的Res-UNet网络识别织物表面瑕疵,并且采用迁移学习算法进一步优化识别模型的参数,实现织物表面瑕疵准确识别。结果表明:本文方法应用下,无论是素色样本,还是花色样本,其识别系数均达到0.9以上,相比基于标签嵌入方法的织物瑕疵识别方法和双路高分辨率转换网络的布匹瑕疵检测方法,本文方法对复杂花色样本的轮廓系数识别更高,适用性更好,识别能力更强。

基于直方图均衡化的毛织物服装印花缺陷检测

为了保证毛织物印花服装的生产质量,提出基于直方图均衡化的毛织物服装印花缺陷检测方法。设置毛织物服装印花不同缺陷特征作为检测标准,利用光学成像原理采集毛织物服装印花图像,通过颜色转换、图像滤波等步骤,实现初始印花图像的预处理;利用直方图均衡化技术提取服装印花图像特征,通过特征匹配确定缺陷状态与类型,实现毛织物服装印花缺陷的检测。测试结果表明,优化设计方法得出缺陷面积检测误差的平均值为0.09 mm^(2),缺陷类型检测错误率较低。

基于多度量多模型图像投票的织物表面瑕疵检测方法

为解决自动化生产线上织物表面瑕疵检测准确率低和计算速度慢的问题,利用织物表面具有周期纹理的特性提出了一种改进的RANSac检测方法,即多度量多模型图像投票。首先将输入图像裁剪为尺寸一致的子图,计算出子图多维度量的输出值矩阵;然后与改进RANSac计算出的无瑕疵背景的多维度量标准值分别对应作差,采用投票得出每张子图的基础分;再将其在4个记数模型下得到的综合评分排序,根据顺序和偏移量在输出端得到外点所代表的瑕疵子图。实验结果表明:在自采样的织物瑕疵数据集上,选择单度量和单模型的预测精度平均可达到90.9%,平均预测时间达到0.139 s,综合多度量多模型投票的平均预测精度可达到92.7%。该算法不需要大量前期数据进行训练,适用于纯色和条纹状织物的实时表面缺陷检测。

基于改进YOLOv8n的织物疵点检测

针对纺织行业中织物疵点大小不一、织物表面图案复杂等问题,提出一种基于改进YOLOv8n的织物疵点检测算法。首先,在主干网络中用CAA模块替换C2f中的Bottleneck模块,应用两个深度条形卷积作为标准大核深度卷积的近似值来减少计算量,并捕获疵点图像多尺度特征;其次,添加LSKA注意力机制,在特征提取中减少织物表面复杂图案的干扰,提升对小目标的检测精度;最后,使用MPDIoU损失函数弥补原始损失函数的局限性,提高模型训练效率。试验结果表明:改进的YOLOv8n模型mAP值达到90.2%,相比于原始YOLOv8n模型提升了5.9个百分点,同时检测速度达到73帧/s。将改进的模型部署至试验平台进行测试,mAP值和检测速度分别为87.4%和65帧/s,可满足纺织企业对织物疵点检测准确性和实时性的需求。

基于YOLOv8n改进的织物疵点检测算法

为了解决织物疵点检测中小目标疵点难以检测的问题,提出了一种基于改进YOLOv8n算法的织物疵点检测系统。首先,在特征融合部分,采用了兼顾速度和精度的GSConv替代原有的卷积核,并引入了Slim⁃neck特征融合模块,使每个特征层能够同时考虑深层特征的语义信息和浅层特征的细节信息,提高了对小目标的特征响应,同时简化了模型并降低了计算复杂度。其次,设计了用于检测小疵点目标的检测层P2,增强了模型对小疵点目标的检测能力,使其更适用于织物疵点检测任务。最后,采用指数滑动样本加权函数(EMA⁃SlideLoss)替代了交叉熵损失函数,以增强模型的类别分类能力,提高训练的稳定性。试验结果表明:在检测20类疵点时,相较于YOLOv8n模型,该研究方法在mAP@0.5方面提高了0.142,同时实现了47.4帧/s的检测速度。改进的YOLOv8n模型对网络的性能提升是有效的。

基于改进甲壳虫全域搜索算法的机织物疵点检测

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。