基于音频与视频的无监督焊接缺陷检测
Unsupervised Welding Defect Detection Using Audio And Video
AI总结：
本文提出利用音频和视频的无监督深度学习方法实现机器人焊接缺陷实时检测，在4000多个样本上对11类缺陷取得平均AUC 0.92的多模态检测效果。
摘要：
在这项工作中，我们探索了人工智能在机器人焊接中的应用。机器人焊接是许多行业中广泛使用的技术，但机器人目前不具备检测焊接过程中因各种原因引入的焊接缺陷的能力。我们描述了如何应用深度学习方法，通过使用麦克风和摄像头记录焊接过程来实时检测焊接缺陷。我们的发现基于我们收集的一个包含4000多个焊接样本的大型数据库，该数据库涵盖了不同的焊接类型、材料和各类缺陷类别。所有深度学习模型均以无监督方式进行训练，因为可能的缺陷空间很大，而且我们数据中的缺陷可能包含偏差。我们证明了从音频和视频两方面对大多数类别的焊接缺陷进行可靠的实时检测是可行的，并且通过结合两种模态可以实现改进。具体而言，多模态方法在我们数据中所有十一种缺陷类型上的平均ROC曲线下面积（AUC）达到0.92。我们在论文结尾按缺陷类型对结果进行了分析，并对未来工作进行了讨论。
英文摘要：
In this work we explore the application of AI to robotic welding. Robotic welding is a widely used technology in many industries, but robots currently do not have the capability to detect welding defects which get introduced due to various reasons in the welding process. We describe how deep-learning methods can be applied to detect weld defects in real-time by recording the welding process with microphones and a camera. Our findings are based on a large database with more than 4000 welding samples we collected which covers different weld types, materials and various defect categories. All deep learning models are trained in an unsupervised fashion because the space of possible defects is large and the defects in our data may contain biases. We demonstrate that a reliable real-time detection of most categories of weld defects is feasible both from audio and video, with improvements achieved by combining both modalities. Specifically, the multi-modal approach achieves an average Area-under-ROC-Curve (AUC) of 0.92 over all eleven defect types in our data. We conclude the paper with an analysis of the results by defect type and a discussion of future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
