In manufacturing processes, surface inspection is a key requirement for quality assessment and damage localization. Due to this, automated surface anomaly detection has become a promising area of research in various industrial inspection systems. A particular challenge in industries with large-scale components, like aircraft and heavy machinery, is inspecting large parts with very small defect dimensions. Moreover, these parts can be of curved shapes. To address this challenge, we present a 2-stage multi-modal inspection pipeline with visual and tactile sensing. Our approach combines the best of both visual and tactile sensing by identifying and localizing defects using a global view (vision) and using the localized area for tactile scanning for identifying remaining defects. To benchmark our approach, we propose a novel real-world dataset with multiple metallic defect types per image, collected in the production environments on real aerospace manufacturing parts, as well as online robot experiments in two environments. Our approach is able to identify 85% defects using Stage I and identify 100% defects after Stage II. The dataset is publicly available at https://zenodo.org/record/8327713

翻译：在制造过程中，表面检测是质量评估与损伤定位的关键要求。因此，自动化表面异常检测已成为各类工业检测系统中具有前景的研究领域。在飞机和重型机械等大型部件制造行业中，一个特殊挑战在于检测具有极小缺陷尺寸的大型部件。此外，这些部件可能具有曲面形状。为应对这一挑战，我们提出了一种结合视觉与触觉感知的两阶段多模态检测流水线。我们的方法通过利用全局视图（视觉）识别和定位缺陷，再利用定位区域进行触觉扫描以识别剩余缺陷，从而融合了视觉与触觉感知的优势。为对我们的方法进行基准测试，我们提出了一个新型真实世界数据集，其中每张图像包含多种金属缺陷类型，该数据集是在实际航空航天制造部件的生产环境中采集的，同时还包括两个环境中的在线机器人实验。该方法在阶段I能识别85%的缺陷，在阶段II后能识别100%的缺陷。该数据集已公开于https://zenodo.org/record/8327713