Industrial defective chips detection using deep convolutional neural network with inverse feature matching mechanism

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL
Waseem Ullah, Samee Ullah Khan, Min Je Kim, Altaf Hussain, Muhammad Munsif, Mi Young Lee, Daeho Seo, Sung Wook Baik
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引用次数: 0

Abstract

The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). Anomaly detection in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increasingly recognize the significance of employing intelligent systems to detect flaws and defects in product parts. However, industrial settings pose several challenges: diverse categories, limited abnormal samples, and vagueness. Hence, there is a growing demand for advanced image anomaly detection techniques within industrial product manufacturing. In this paper, an intelligent industrial defective chips detection framework is proposed which mainly consists of three core components. First, the convolutional features of the efficient backbone model is effectively utilized to balance the computational complexity and performance of industrial resource-constrained devices. Secondly, a novel inverse feature matching followed by masking method is proposed to enhance the explanability that localizes the abnormal regions of the abnormal chips. Finally, to evaluate our proposed method a comprehensive ablation study is conducted, where different machine learning and deep learning algorithms are analyzed to claim the superiority of our method. Furthermore, to help the research community, a benchmark dataset is collected from real-world industry manufacturing for defective chip detection. The empirical results from the dataset demonstrate the strength and effectiveness of the proposed model compared to the other models.
利用具有反向特征匹配机制的深度卷积神经网络检测工业缺陷芯片
人们对高质量工业产品的需求日益增长,因此对图像异常检测(AD)的重视程度也与日俱增。工业产品中的异常检测是一项艰巨的研究挑战,需要应用复杂的技术来准确识别和处理与预期标准的偏差。制造商日益认识到采用智能系统检测产品部件缺陷和瑕疵的重要性。然而,工业环境带来了一些挑战:种类繁多、异常样本有限以及模糊性。因此,工业产品制造领域对先进图像异常检测技术的需求日益增长。本文提出了一种智能工业缺陷芯片检测框架,主要由三个核心部分组成。首先,有效利用高效骨干模型的卷积特征,平衡工业资源受限设备的计算复杂度和性能。其次,我们提出了一种新颖的反向特征匹配和掩蔽方法,以增强对异常芯片异常区域定位的可解释性。最后,为了评估我们提出的方法,我们进行了一项全面的烧蚀研究,分析了不同的机器学习和深度学习算法,以证明我们方法的优越性。此外,为了帮助研究界,我们还从现实世界的工业生产中收集了一个基准数据集,用于缺陷芯片检测。数据集的实证结果表明,与其他模型相比,我们提出的模型更强大、更有效。
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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