Automatic Vision-Based Online Inspection System for Broken-Filament of Carbon Fiber With Multiscale Feature Learning

This article aims at solving the problem of broken-filament online detection and tracking during the process of carbon fiber production. Owing to the complex background and unique broken-filament shape, it is challenging to achieve the broken-filament online detection with high accuracy via classical machine vision methods, even general convolutional neural network (CNN)-based methods. Moreover, the carbon fiber strands are usually merged during the winding process, which would lead to the broken-filament location and tracking errors due to wrong segmentation and numbering of carbon fiber strands. To address these problems, we propose a vision-based method to achieve broken-filament online detection and tracking. First, an image collection module is designed to capture high resolution carbon fiber strand data. Then, a novel broken-filament detection network (BFDNet) is proposed to detect broken-filaments, including a feature learning part and a new anchor generation scheme, which is implemented based on the RetinaNet framework. Especially, the feature learning part contains several multiscale kernel fusion blocks (MKFBs) that are composed of convolution kernels of different sizes. Finally, a pixel projection method is proposed to segment and number carbon fiber strands for tracking. Our method achieves satisfactory performance for broken-filament online detection in terms of accuracy compared with several state-of-the-art methods designed for feature learning based on the classical object detection framework. Moreover, our method has been applied in practical applications for broken-filament inspection tasks in factories.