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The identification of animal fibers has always been an important topic in the textile field. Therefore, it is very important to identify fiber types accurately and quickly for textile fiber quality inspection. Although some traditional image-based fiber recognition techniques can identify one to four fibers in an image, the methods usually need to segment the fibers in the image into a single fiber image, which is a time-consuming process. Therefore, the FiberNet model is proposed to automate this task. Different from other methods, this study used the case segmentation model for the first time to detect multiple fibers and their species. FiberNet was developed based on BlendMask, introducing a denoising module and an attention module to improve the final detection accuracy and speed. Therefore, the model performance and identification accuracy of FiberNet are higher than those of the fiber detection algorithm, with identification accuracy of up to 99.51%.

FiberNet: auto-detection of multi-fibers in microscopic image using instance segmentation