This research focuses on trading card quality inspection, where defects have a significant effect on both the quality inspection and grading. The present inspection procedure is subjective which means the grading is sensitive to mistakes made by individuals. To address this, a deep neural network based on transfer learning for automated defect detection is proposed with a particular emphasis on corner grading which is a crucial factor in overall card grading. This paper presents an extension of our prior study, in which we achieved an accuracy of 78% by employing the VGG-net and InceptionV3 models. In this study, our emphasis is on the DenseNet model where convolutional layers are used to extract features and regularisation methods including batch normalisation and spatial dropout are incorporated for better defect classification. Our approach outperformed prior findings, as evidenced by experimental results based on a real dataset provided by our industry partner, achieving an 83% mean accuracy in defect classification. Additionally, this study investigates various calibration approaches to fine-tune the model confidence. To make the model more reliable, a rule-based approach is incorporated to classify defects based on confidence scores. Finally, a human-in-the-loop system is integrated to inspect the misclassified samples. Our results demonstrate that the model’s performance and confidence are expected to improve further when a large number of misclassified samples, along with human feedback, are used to retrain the network.

中文翻译：

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交易卡的自动角分级：通过深度学习进行缺陷识别和置信度校准


本研究的重点是交易卡质量检查，其中缺陷对质量检查和分级都有显着影响。目前的检查程序是主观的，这意味着分级对个人所犯的错误很敏感。为了解决这个问题，提出了一种基于迁移学习的深度神经网络，用于自动缺陷检测，特别强调角点分级，这是整体卡片分级的关键因素。本文介绍了我们之前研究的扩展，其中我们通过使用 VGG-net 和 InceptionV3 模型实现了 78% 的准确率。在本研究中，我们的重点是 DenseNet 模型，其中使用卷积层来提取特征，并结合批量归一化和空间 dropout 等正则化方法，以实现更好的缺陷分类。基于我们的行业合作伙伴提供的真实数据集的实验结果证明，我们的方法优于之前的发现，缺陷分类的平均准确度达到 83%。此外，本研究还研究了各种校准方法来微调模型置信度。为了使模型更加可靠，采用了基于规则的方法，根据置信度分数对缺陷进行分类。最后，集成人机交互系统来检查错误分类的样本。我们的结果表明，当使用大量错误分类的样本以及人类反馈来重新训练网络时，模型的性能和置信度有望进一步提高。