Division of Mechanical and Aerospace Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
Sambo Technology, 90 Centum Jungang-ro, Haeundae-gu, Busan 48059, Republic of Korea.
Sensors (Basel). 2023 Jun 20;23(12):5768. doi: 10.3390/s23125768.
Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.
缺陷检测对于确保工业制造的质量和效率一致性非常重要。最近，集成基于人工智能（AI）的检测算法的机器视觉系统在各种应用中表现出了很有前景的性能，但实际上，它们经常受到数据不平衡的影响。本文提出了一种使用一类分类（OCC）模型的缺陷检测方法，以处理不平衡数据集。提出了一种由全局和局部特征提取器网络组成的双流网络架构，可以缓解 OCC 的表示崩溃问题。通过将面向对象的不变特征向量与面向训练数据的局部特征向量相结合，所提出的双流网络模型可以防止决策边界崩溃到训练数据集，并获得适当的决策边界。所提出的模型在汽车安全气囊支架焊接缺陷检测的实际应用中得到了验证。通过使用在受控实验室环境和生产现场采集的图像样本，阐明了分类层和双流网络架构对整体检测精度的影响。结果与之前的分类模型进行了比较，表明所提出的模型可以分别将准确性、精度和 F1 分数提高 8.19%、10.74%和 4.02%。
