In textile factories, the most typical warp-knitted fabric defects include point defects, holes, and color differences. Traditional manual inspection methods are inefficient for detecting these defects. Existing intelligent inspection systems often have a single function. Factories require a real-time inspection system that can detect common defects and color difference. The YOLO (you only look once) neural network is faster than the two-stage neural network and has lower hardware requirements. The system’s color difference detection algorithm compares the color difference between the standard image and the image to be measured and records where the color difference value is exceeded. Finally, the comparison of the factory application proves that the designed system has good real-time performance and accuracy and can meet the fabric inspection requirements of warp-knitted fabric factories.

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 F1 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.

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.