During a practical detection process, insufficient defect data, unbalanced defect types and the high cost of defect labeling can present problems. Therefore, it often takes considerable time and labor to collect actual samples to improve the accuracy of defect classification and recognition. In this paper, we propose a weak supervision machine vision detection method based on artificial defect simulation. First, four typical mobile phone screen defects – scratches, floaters, light stains and severe stains – are simulated by the proposed synthesis algorithms, and an artificial defect database is created. Next, the artificial dataset is applied to a deep learning recognition algorithm, and an initial model is trained. Then, the collected actual defects are augmented due to the insufficient training quantity. The augmented actual defects are then applied as the training data, and the initial model is retrained by fine tuning. Finally, the well-retrained model is used for defect recognition. The experimental results demonstrate that satisfactory performance is achieved with the proposed detection method.

在实际的检测过程中，缺陷数据不足，缺陷类型不平衡以及缺陷标记的高昂成本可能会带来问题。因此，通常需要花费大量时间和精力来收集实际样本，以提高缺陷分类和识别的准确性。本文提出了一种基于人工缺陷仿真的弱监督机器视觉检测方法。首先，通过提出的综合算法模拟了四种典型的手机屏幕缺陷-划痕，浮法，浅色斑点和重度斑点，并创建了一个人工缺陷数据库。接下来，将人工数据集应用于深度学习识别算法，并训练初始模型。然后，由于训练量不足，收集到的实际缺陷增加了。然后将增加的实际缺陷用作训练数据，并通过微调对初始模型进行训练。最后，将训练有素的模型用于缺陷识别。实验结果表明，所提出的检测方法取得了令人满意的性能。