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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 N et al gorithm reached 97.7%, and the detection speed is improved by 3–4 times compared with that of the the conventional manual inspection method.

Warp-knitted vamp fabric quality detection algorithm based on improved MatchNet