Computer vision provides effective solutions in many imaging relation problems, including automatic image segmentation and classification. Artificially trained models can be employed to tag images and identify objects spontaneously. In large-scale manufacturing, industrial cameras are utilized to take constant images of components for several reasons. Due to the limitations caused by motion, lens distortion, and noise, some defective images are captured, which are to be identified and separated. One common way to address this problem is by looking into these images manually. However, this solution is not only very time-consuming but is also inaccurate. The paper proposes a deep learning-based artificially intelligent system that can quickly train and identify faulty images. For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task. In order to eliminate the chances of overfitting, the proposed model also employed Dropout technology to adjust the network. The experimental study reveals that the system can precisely classify the normal and defective images with an accuracy of over 91%.

中文翻译：

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使用卷积神经网络对工业零部件进行缺陷图像分类

计算机视觉为许多成像关系问题提供了有效的解决方案，包括自动图像分割和分类。可以使用人工训练的模型来标记图像并自发地识别对象。在大规模制造中，出于多种原因，工业相机被用来拍摄组件的恒定图像。由于运动、镜头畸变和噪声等因素的限制，会捕捉到一些有缺陷的图像，需要对其进行识别和分离。解决此问题的一种常见方法是手动查看这些图像。然而，这种解决方案不仅非常耗时，而且不准确。该论文提出了一种基于深度学习的人工智能系统，可以快速训练和识别错误图像。以此目的，使用基于 PyTorch 框架的预训练卷积神经网络从数据集中提取区分特征，然后将其用于分类任务。为了消除过拟合的可能性，所提出的模型还采用了 Dropout 技术来调整网络。实验研究表明，该系统可以对正常图像和缺陷图像进行精确分类，准确率超过91%。