Streamline video-based automatic fabric pattern recognition using Bayesian-optimized convolutional neural network

Examining fabric weave patterns (FWPs) is connected to image-based surface texture feature (STF) acquisition, which can be difficult due to the structural complexity of woven fabrics. Randomly capturing static images may not correlate with the entire STF of a fabric. Traditionally, FWPs analysis is conducted by human vision, which causes an intensive cognitive load. Ultimately, the human vision-based cognitive load leads to ineffective quality inspection and error-prone FWPs analysis results. Given the above challenges, this study proposes a new streamlined video-based FWPs recognition method by incorporating Bayesian-optimized convolutional neural network (Bayes Opt-CNN). Essentially, this method is capable of leveraging the spatiotemporal features of the fabric's intricate surface structure. In this study, to validate the effectiveness of the proposed method, seven types of fabric structures were captured as streamline videos, which were then converted into sequences of image frames. Subsequently, the Bayesian optimization process was introduced to select the best hyperparameters during CNN-based supervised learning for pattern recognition. The evaluation demonstrates that the proposed method outperforms the benchmark method for identifying FWPs.