| 基于视觉的汽车装配件缺陷检测研究进展 |
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| 引用本文： | 张瀚丹,吴一全.基于视觉的汽车装配件缺陷检测研究进展[J].仪器仪表学报,2023,44(8):1-20. |
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| 作者姓名： | 张瀚丹 吴一全 |
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| 作者单位： | 1.南京航空航天大学电子信息工程学院 |
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| 基金项目： | 国家自然科学基金(61573183)项目资助 |
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| 摘 要： | 汽车装配件的缺陷检测是汽车制造流程中的重要环节,不仅可以提升产品质量,降低退货率,避免成本浪费,还可以为
驾驶人员提供安全保障。 最早的缺陷检测依靠专家经验,准确度低,人力成本大,而无损检测技术依靠介质,且效率不高。 引入
机器视觉不仅可以平衡检测精度和效率的问题,还能提高检测系统的鲁棒性,是最有发展潜力的缺陷检测技术之一。 本文首先
给出了视觉缺陷检测的定义和主要流程,简述了视觉缺陷检测系统中的图像采集硬件,然后从常用的缺陷分割方法、特征提取
方法、卷积神经网络 3 个方面综述了近年来汽车装配件缺陷检测的研究进展,并对比分析了相关方法的优缺点。 接着把汽车的
装配件大致分为轮毂轮胎、车身漆面、零件、发动机等 4 类,总结了缺陷类型及其缺陷检测算法的研究现状。 随后介绍了与汽车
工业相关的 10 个数据集和缺陷检测性能评价指标。 最后指出针对汽车装配件的缺陷检测目前面临着诸多方面的技术挑战,并
对进一步的工作进行了展望。
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| 关 键 词： | 汽车装配件 缺陷检测 机器视觉 深度学习 性能评价指标 |
Research progress of vehicle assembly defect detection methods based on vision |
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| Zhang Handan,Wu Yiquan.Research progress of vehicle assembly defect detection methods based on vision[J].Chinese Journal of Scientific Instrument,2023,44(8):1-20. |
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| Authors: | Zhang Handan Wu Yiquan |
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| Affiliation: | 1.College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics |
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| Abstract: | The defect detection of automotive assembly parts is an important part in the automotive manufacturing process, which can not
only improve product quality, reduce the return rate, avoid cost waste, but also provide safety protection for drivers. The earliest defect
detection relies on expert experience, which is low accuracy and high labor cost. The nondestructive testing technology relies on media
and is not efficient. The introduction of machine vision can not only balance the problem of detection accuracy and efficiency, but also
improve the robustness of the detection system, which is one of the most promising defect detection technologies. This article firstly gives
the definition and main process of visual defect detection, briefly introduces the hardware of image acquisition in visual defect detection
system. Then, the research progress of automobile assembly defect detection in recent years is reviewed from three aspects of commonly
used defect segmentation methods, feature extraction methods and convolutional neural networks. The advantages and disadvantages of
related methods are compared and analyzed. The automobile assembly parts are roughly divided into four categories, such as wheel tires,
body paint, parts and engines. The research status of defect types and defect detection algorithms are summarized. Next, 10 data sets
related to the automobile industry and defect detection performance evaluation indicators are introduced. Finally, it is pointed out that the
defect detection of automobile assembly is faced with many technical challenges, and the prospect of further work is given. |
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| Keywords: | automobile assembly defect detection machine vision deep learning performance evaluation index |
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