表面缺陷视觉快速检测技术研究 | |
黄亦斌 | |
2019 | |
页数 | 130 |
学位类型 | 博士 |
中文摘要 | 产品的表面质量是其商品价值和使用价值的重要保障,也与企业的利益和信誉息息相关。基于机器视觉的表面缺陷检测技术能在高速生产线上剔除不合格产品,在保证产品质量的同时节省大量的人力成本。所以,视觉表面缺陷检测技术有着重要的研究意义和很高的实用价值,也是智能制造技术的重要组成部分。本文针对视觉缺陷检测中尚未得到解决的若干关键技术问题展开研究,在考虑缺陷检测算法准确性的同时也充分考虑了高速生产线对实时性的要求。本文的主要工作如下: (1) 研究显著性检测模型在缺陷检测领域的应用。提出了一个使用增强、互补或削弱等策略,融合图像多个线索的缺陷显著性检测模型。与传统的显著性检测模型相比,该方法具有更强的特征描述能力,可以检测出复杂多变的缺陷。针对磁瓦缺陷检测问题,改进 U-Net 卷积神经网络检测缺陷。在磁瓦缺陷检测任务中,该深度学习模型优于传统缺陷检测模型。
(3) 针对大幅面、高分辨率图像的微小缺陷检测问题,设计了一种多任务型卷积神经网络。利用快速下采样层,使网络在保留更多的图像细节的同时,有效地减少网络对显存的占用;采用多任务监督策略,使得缺陷二分类和多分类的性能都得以提升;使用常规增广、随机掩模和条件 Inpaint 等数据增广方法,解决了高分辨图像样本不均衡和数据不足等问题。本章还分析了不同类型的损失函数对网络性能的影响,在高分辨率的布匹缺陷数据集上验证了本文提出的多任务型卷积神经网络的有效性。与其它多种相关方法相比,在有限的硬件资源下,本章所提出的方法获得了最高的准确率,且算法的运算速度达到了实时检测高分辨率图像的需求。 本文的最后还简单总结了本文的工作成果,并讨论了在本文的研究基础上可以进一步开展的研究工作。 |
英文摘要 | The surface quality of products guarantees the commodity value and use-value of the products, and it is closely related to the interests and reputation of enterprises. Machine vision based surface defect inspection technology can be used to filter out unqualified products on high-speed production lines, and this technology also saves a lot of labor cost on quality inspection. This dissertation focuses on several key technical problems that have not been solved in visual defect inspection. We are committed to improving the accuracies of defect inspection algorithms, and also fully consider the real-time requirements of high-speed production lines. The major works and contributions of this dissertation are as follows:
(1) The feasibility of applying the saliency detection model to surface defect inspection is studied. A defect detection model fusing multiple saliency cues with strategies including enhancement, complementation, or weakening is proposed. Compared with the traditional saliency detection model, this method has stronger representation ability and can detect complex and variable defects. And an improved U-Net is used to inspect magnetic tile defects. This deep learning approach achieves the best detection result in the magnetic tile defect inspection task, against other traditional models. (3) A multi-task CNN is designed to detect small defects in high-resolution images. The fast down-sampling layer effectively reduces the graphics memory footprint while retaining image details; multi-task supervision improves the binary and multi-class defect classification performance; and data augmentation methods such as conventional augmentation, random mask, and conditional inpaint solve the problem of data imbalance and data insufficiency. The effect of different loss functions on network performance is also analyzed. The effectiveness of the proposed multi-task CNN is tested on the high-resolution fabric defect dataset. Compared with other related methods, the proposed model achieves the highest accuracy with limited hardware resources, and the inference speed meets the needs of real-time inspection of high-resolution images. (4) Two kinds of high-precision linear structured light 3D vision sensors are designed to solve the problem that 2D images cannot reliably detect 3D slight deformation defects. By improving the Steger algorithm or using the FPGA acceleration, the scanning frequency of the 3D vision sensor is improved. The proposed method can achieve 280 Hz scanning frequency on a common CPU and can satisfy the requirement of high-frequency scanning scenarios. In this chapter, characteristic line chamfer matching and improved FDCM method are proposed for registration of 3D depth images, which can inspect sub-millimeter level 3D deformation defects and greatly reduce the computational complexity of FDCM method, so that the 3D defect inspection system can meet the real-time requirements.
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关键词 | 视觉表面缺陷检测, 显著性检测,卷积神经网络, 线结构光,三维配准 |
语种 | 中文 |
文献类型 | 学位论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23815 |
专题 | 智能制造技术与系统研究中心 智能制造技术与系统研究中心_智能机器人 |
推荐引用方式 GB/T 7714 | 黄亦斌. 表面缺陷视觉快速检测技术研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2019. |
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