Automatic Vision-Based Online Inspection System for Broken-Filament of Carbon Fiber With Multiscale Feature Learning
Dawei Li, Shiyan Hua, Zhongyu Li, Xiaoxi Gong, Jun Wang
Abstract
This paper 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 CNN-based methods. Moreover, the carbon fiber strands are usually merged during the winding process, which would lead to 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. At 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 Multi-scale Kernel Fusion Blocks (MKFBs), which 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 when 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.