  Visual defect detection (VDD) for high-mix low-volume production of
non-convex metal objects, such as high-pressure cylindrical piping joint parts
(VDD-HPPPs), is challenging because subtle difference in domain (e.g., metal
objects, imaging device, viewpoints, lighting) significantly affects the
specular reflection characteristics of individual metal object types. In this
paper, we address this issue by introducing a tailor-made VDD framework that
can be automatically adapted to a new domain. Specifically, we formulate this
adaptation task as the problem of network architecture search (NAS) on a deep
object-detection network, in which the network architecture is searched via
reinforcement learning. We demonstrate the effectiveness of the proposed
framework using the VDD-HPPPs task as a factory case study. Experimental
results show that the proposed method achieved higher burr detection accuracy
compared with the baseline method for data with different training/test domains
for the non-convex HPPPs, which are particularly affected by domain shifts.
