无纺布疵点实时检测技术与系统设计

Non-woven Fabric Real-time Defects Detection Method and Framework Design

无纺布生产过程中产生的疵点会严重影响产品质量并限制生产效率.提高疵点检测的自动化程度对于无纺布的生产效率和质量管控至关重要.传统疵点检测方法难以应对纹理、疵点类型以及环境变化等问题,限制了其应用范围.近年来基于卷积神经网络的方法在疵点检测领域得到了广泛应用,具有泛化性强、准确度高的特点.但是在无纺布生产过程中,布匹宽度大、速度快的特点会产生大量图像数据,基于卷积神经网络的方法难以实现实时检测.针对上述难题,本文提出了一种基于最大稳定极值区域分析与卷积神经网络协同的疵点实时检测方法,并设计了分布式计算处理架构应对数据流过大的问题.在实际生产部署应用中,本文所设计的系统与算法无需使用专用计算硬件(GPU、FPGA等),通过8台工控机与16路工业摄像头对复卷机上布宽2.8 m、速度30 m/min的无纺布进行分布式实时在线检测,大幅度提高无纺布生产中疵点检测的自动化程度与效率.本文所提出的系统能够实现对0.3 mm以上疵点召回率100%,对0.1 mm丝状疵点召回率98.8%.

The defects generated during the production process of non-woven fabric will seriously affect the quality and limit the efficiency.How to improve the automatic degree of non-woven fabric defects detection plays a significant role.The traditional defects detection methods cannot deal with the changing of texture,defects type and environments,which limits the application scope.In recent years,the methods based on convolutional neural networks(CNNs)have been widely used in the field of defects detection,which are shown to have the characteristics of strong generalization ability and high accuracy.However,in the non-woven fabric production process,the large width and high speed of cloth will introduce huge amount of image data,which makes it difficult for CNN based methods to achieve real-time detection.In this paper,a real-time defects detection method based on stable extremal region analysis and CNN is proposed,and a distributed computing architecture is designed to handle the problem of large image data stream.In the actual deployment application,the system designed in this paper does not need specific computing hardware(GPU,FPGA,etc.).8 industrial computers and 16 industrial cameras are coupled together in a distribution scheme to finish real-time defects detection of non-woven fabric rewinder with cloth width 2.8 m and speed 30 m/min,which greatly improves the automation and production efficiency.The system proposed in this paper can achieve 100%recall rate of punctiform defects above 0.3 mm and 98.8%recall rate of 0.1 mm filiform defects.