PCB Soldering Defect Inspection Using Multitask Learning under Low Data Regimes,Advanced Intelligent Systems

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PCB Soldering Defect Inspection Using Multitask Learning under Low Data Regimes
Advanced Intelligent Systems ( IF 6.8 ) Pub Date : 2023-09-21 , DOI: 10.1002/aisy.202300364
Sik-Ho Tsang ₁ , Zhaoqing Suo _{1,

2} , Tom Tak-Lam Chan ₁ , Huu-Thanh Nguyen ₁ , Daniel Pak-Kong Lun _{1,

2}

Affiliation

To increase the reliability of the printed circuit board (PCB) manufacturing process, automated optical inspection is often employed for soldering defect detection. However, traditional approaches built on handcrafted features, predefined rules, or thresholds are often susceptible to the variation of the acquired images’ quality and give unstable performances. To solve this problem, a deep learning-based soldering defect detection method is developed in this article. Like many real-life deep learning applications, the number of available training samples is often limited. This creates a challenging low-data scenario, as deep learning typically requires massive data to perform well. To address this issue, a multitask learning model is proposed, namely, PCBMTL, that can simultaneously learn the classification and segmentation tasks under low-data regimes. By acquiring the segmentation knowledge, classification performance is substantially improved with few samples. To facilitate the study, a soldering defect image dataset, namely, PCBSPDefect, is built. It focuses on the dual in-line packages (DIP) at the PCB back side, DIP at the PCB front side, and flat flexible cables. Experimental results show that the proposed PCBMTL outperforms the best existing approaches by over 5–17% of average accuracy for different datasets.

中文翻译：

在低数据条件下使用多任务学习进行 PCB 焊接缺陷检查

为了提高印刷电路板 (PCB) 制造过程的可靠性，通常采用自动光学检查来检测焊接缺陷。然而，基于手工特征、预定义规则或阈值的传统方法通常容易受到所获取图像质量变化的影响，从而导致性能不稳定。为了解决这个问题，本文开发了一种基于深度学习的焊接缺陷检测方法。与许多现实生活中的深度学习应用程序一样，可用训练样本的数量通常是有限的。这创造了一个具有挑战性的低数据场景，因为深度学习通常需要大量数据才能表现良好。为了解决这个问题，提出了一种多任务学习模型，即 PCBMTL，它可以在低数据情况下同时学习分类和分割任务。通过获取分割知识，可以在少量样本的情况下大幅提高分类性能。为了便于研究，建立了焊接缺陷图像数据集，即PCBSPDefect。重点关注 PCB 背面的双列直插式封装 (DIP)、PCB 正面的 DIP 以及扁平柔性电缆。实验结果表明，对于不同数据集，所提出的 PCBMTL 的平均准确度比现有最佳方法高出 5-17% 以上。

更新日期：2023-09-21

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