用音视频数据实现焊接缺陷实时无监督检测
Unsupervised Welding Defect Detection Using Audio And Video
- 通过麦克风和摄像头采集音视频，用无监督深度学习识别缺陷
- 多模态融合使11类缺陷平均AUC达0.92，效果优于单一模态
- 适合工业质检场景，尤其对缺陷种类多、标注难的生产环境
本文探索AI在机器人焊接中的应用。当前机器人无法检测因多种原因产生的焊接缺陷。我们基于超过4000个焊接样本构建的大规模数据库，涵盖不同焊缝类型、材料及多种缺陷类别，采用无监督深度学习方法，通过麦克风与相机实时记录焊接过程，实现缺陷检测。所有模型均以无监督方式训练，以应对缺陷空间庞大且数据可能存在偏见的问题。实验表明，仅用音频或视频即可实现大多数缺陷类别的可靠实时检测，多模态融合进一步提升性能，整体在11类缺陷上的平均受试者工作特征曲线下面积（AUC）达到0.92。论文还按缺陷类型分析结果，并讨论未来方向。
原文摘要 · Abstract (English)
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.
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