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Fabric defect detection plays a crucial role in the production process of the textile industry. Vision-based inspection methods have emerged as an inevitable trend due to their lower labor costs and high detection efficiency. As the accuracy requirements for fabric defect detection, methods must not only identify and locate defects accurately but also describe the morphological features of the defects. This poses a challenge for the algorithm's design, as it must consider both the semantic and texture information of the fabric. In this paper, we propose an end-to-end dual-path segmentation network called DPNet for fabric defect detection, which can extract and fuse both semantic and texture information to achieve high accuracy. The proposed framework consists of two paths: the semantic path, which has narrow but deep layers to obtain high-dimensional features, and the texture path, which has wide and dense layers to extract low-level details. To enhance the interaction between semantic and texture features, a crossed attention fusion module has been developed. Evaluations show that the proposed method outperforms other methods on different datasets in terms of mIoU, with results of 75.84% for Ngan and 70.69% for AITEX. In addition, we developed an inspection platform and tested the proposed method online. We found that it can achieve online detection at a speed of 40 m/min, making it well-suited for practical production environments. [ABSTRACT FROM AUTHOR]

To solve the problem of low efficiency and high cost of conventional manual inspection of warp-knitted vamp fabrics, an algorithm for quantity statistics and quality inspection of warp-knitted vamps based on an improved MatchNet model is proposed. The noise in the warp-knitted vamp images was removed using median filtering and the filtering speed was accelerated using the mean division method. To extract the vamp image from the cloth image, an adaptive thresholding watershed algorithm was designed to remove the background texture information from the warp-knitted vamp and extract the vamp features. Based on the MatchNet model, the feature extraction network for warp-knitted vamps was redesigned and the model was optimised using the AdaDelta algorithm to improve the model training efficiency. A sampler was used to generate a sample set of warp-knitted vamp data with a balanced number of positive and negative samples to train the improved MatchNet model. A warp-knitted vamp fabric detection platform was built to verify the effectiveness and real-time performance of the improved algorithm. The MatchNet model and the improved MatchNet model algorithm were applied to three different vamps to perform comparison test verification. The results show that the average detection accuracy of the improved Match Net algorithm reached 97.7%, and the detection speed is improved by 3–4 times compared with that of the the conventional manual inspection method. [ABSTRACT FROM AUTHOR]

The complex background pattern of color-patterned fabrics, the small target of some defects, the difficulty separating them from the background, and the extreme aspect ratio present challenges for their automated, real-time detection. To solve the above problems, the YOLOv5s-based color-patterned fabric defect detection algorithm was proposed by combining lightweight modules. For the small target defects of color-patterned fabrics, coordinate attention was introduced in the feature extraction part to guide the model to focus entirely on the target defect area and suppress the background noise of color-patterned fabrics. Meanwhile, the bidirectional feature pyramid network was introduced in the feature fusion part to give different fusion weights to the extracted feature maps, improve the efficiency of feature fusion, and guide the model further to distinguish the fabric defects from the color-patterned background. Finally, the k -means algorithm was used to generate anchor boxes for the extreme aspect ratio of fabric defects to improve the training efficiency and accuracy of the model. Self-built datasets were experimented with to verify the improved model's detection effect. The results show that the improved YOLOv5s model can reach 87.7% and 0.881 in mean average precision and F 1 score, which are 2.3% and 0.02 better than the original model, respectively. The detection speed of the improved YOLOv5s model reached 60.24 frames per second (GPU 1660). After deployment on the fabric defect detection platform, the speed of detecting color-patterned fabrics can reach 15 m/min. [ABSTRACT FROM AUTHOR]

The current color difference online detection algorithm has poor real-time performance. The noise affects color difference monitoring and sampling detection results are different from online detection. Therefore, a color difference online detection algorithm is studied. Firstly, the Flat Field and Perfect Reflector Method (PRM) are used to correct the color illumination uniformity of warp knitted fabric images. The image pyramid principle is used to down-sample the images to improve the online detection speed. Secondly, the principle of the histogram intersection method is used to pre-judge the color difference of warp knitted fabric image, and judge whether there is a color difference. The improved K-means algorithm is used to analyze the color types of the fabric, and the CMC (l : c) calculation formula is used to quantitatively calculate the color difference value. Finally, the algorithm has been verified in the factory. The speed of fabric inspection using the algorithm can reach 0.75–0.8m/s, and the color difference detection algorithm is compared with manual detection. The results show that the method can meet the real-time requirements of the system and it is consistent with the sampling test, and the detection speed increased from 0.2 m / s to 0.8 m / s. [ABSTRACT FROM AUTHOR]

The background pattern of patterned fabrics is complex, which has a great interference in the extraction of defect features. Traditional machine vision algorithms rely on artificially designed features, which are greatly affected by background patterns and are difficult to effectively extract flaw features. Therefore, a convolutional neural network (CNN) with automatic feature extraction is proposed. On the basis of the two-stage detection model Faster R-CNN, Resnet-50 is used as the backbone network, and the problem of flaws with extreme aspect ratio is solved by improving the initialization algorithm of the prior frame aspect ratio, and the improved multi-scale model is designed to improve detection of small defects. The cascade R-CNN is introduced to improve the accuracy of defect detection, and the online hard example mining (OHEM) algorithm is used to strengthen the learning of hard samples to reduce the interference of complex backgrounds on the defect detection of patterned fabrics, and construct the focal loss as a loss function to reduce the impact of sample imbalance. In order to verify the effectiveness of the improved algorithm, a defect detection comparison experiment was set up. The experimental results show that the accuracy of the defect detection algorithm of patterned fabrics in this paper can reach 95.7%, and it can accurately locate the defect location and meet the actual needs of the factory. [ABSTRACT FROM AUTHOR]