  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
