With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.

Due to the significant potential values in commercial and social applications, clothing image recognition has recently become a research hotspot, among which clothing attribute recognition is an important content. However, the large variations in the appearance and style of clothing and the image’s complex forming conditions make the task challenging. Moreover, a generic treatment with deep convolutional neural networks cannot provide an ideal solution. Instead of using CNNs for classification, we proposed a novel approach based RCNN framework for the recognition task. Firstly, we apply the modified selective search algorithm to extract the region proposal. Then, the Inception-ResNet V1 model with L-Softmax is employed to represent images and identify their categories. After Soft-NMS, we use a simple neural network to correct the boundary of region box. To evaluate the performance of the framework, a dataset including about 100,000 shirt images was built. The experimental result show that our proposed framework achieved promising overall labeling rate, precision and recall of 87.77%, 73.59% and 83.84%. In addition, comparative experiments demonstrate the superiority of the proposed framework.

Due to the potential values in many areas such as e-commerce and inventory management, fabric image retrieval, which is a special case in Content Based Image Retrieval (CBIR), has recently become a research hotspot. It is also a challenging issue with serval obstacles: variety and complexity of fabric appearance, high requirements for retrieval accuracy. To address this issue, this paper proposes a novel approach for fabric image retrieval based on multi-task learning and deep hashing. According to the cognitive system of fabric, a multi-classification-task learning model with uncertainty loss and constraint is presented to learn fabric image representation. Then we adopt an unsupervised deep network to encode the extracted features into 128-bits hashing codes. Further, the hashing codes are regarded as the index of fabrics image for image retrieval. To evaluate the proposed approach, we expanded and upgraded the dataset WFID, which was built in our previous research specifically for fabric image retrieval. The experimental results show that the proposed approach outperforms the state-of-the-art.

The traditional manual defect detection method has low efficiency and is time-consuming and laborious. To address this issue, this paper proposed an automatic detection framework for fabric defect detection, which consists of a hardware system and detection algorithm. For the efficient and high-quality acquisition of fabric images, an image acquisition assembly equipped with three sets of lights sources, eight cameras, and a mirror was developed. The image acquisition speed of the developed device is up to 65 m per minute of fabric. This study treats the problem of fabric defect detection as an object detection task in machine vision. Considering the real-time and precision requirements of detection, we improved some components of CenterNet to achieve efficient fabric defect detection, including the introduction of deformable convolution to adapt to different defect shapes and the introduction of i-FPN to adapt to defects of different sizes. Ablation studies demonstrate the effectiveness of our proposed improvements. The comparative experimental results show that our method achieves a satisfactory balance of accuracy and speed, which demonstrate the superiority of the proposed method. The maximum detection speed of the developed system can reach 37.3 m per minute, which can meet the real-time requirements.

The application of content-based image retrieval method aims at retrieving similar fabric images and obtaining the existing process parameters to guide production. The process of sample analysis, trial weaving, and proofing can be eliminated in sample imitation production to give full play to the advantages of historical production experience and improve the core competitiveness of enterprises. By investigating and analyzing the applications of content-based image retrieval method technology in fabric retrieval, this article provides a detailed classification and summary of the existing fabric retrieval methods using content-based image retrieval method from six aspects: image preprocessing, feature extraction, similarity measurement, retrieval strategy, dataset construction, and evaluation metrics in the common framework of content-based image retrieval method. The advantages and disadvantages of different methods are analyzed and compared. Finally, the urgent problems and future research directions of fabric image retrieval are discussed, providing ideas for scholars to further study the retrieval methods. Taking fabric as the medium, this article combs the industrial application research and development process of content-based image retrieval method technology, which is helpful to understand the application examples of computer technology and provide research ideas for the application of different computer technologies in the textile industry.