In many manufacturing settings, annotating data for machine learning and
computer vision is costly, but synthetic data can be generated at significantly
lower cost. Substituting the real-world data with synthetic data is therefore
appealing for many machine learning applications that require large amounts of
training data. However, relying solely on synthetic data is frequently
inadequate for effectively training models that perform well on real-world
data, primarily due to domain shifts between the synthetic and real-world data.
We discuss approaches for dealing with such a domain shift when detecting
defects in X-ray scans of aluminium wheels. Using both simulated and real-world
X-ray images, we train an object detection model with different strategies to
identify the training approach that generates the best detection results while
minimising the demand for annotated real-world training samples. Our
preliminary findings suggest that the sim-2-real domain adaptation approach is
more cost-efficient than a fully supervised oracle - if the total number of
available annotated samples is fixed. Given a certain number of labeled
real-world samples, training on a mix of synthetic and unlabeled real-world
data achieved comparable or even better detection results at significantly
lower cost. We argue that future research into the cost-efficiency of different
training strategies is important for a better understanding of how to allocate
budget in applied machine learning projects.

使用合成数据与未标注的真实数据进行训练可在较低成本下实现与标注真实数据相媲美甚至更好的缺陷检测结果。

通过使用模拟数据来减少数据采集和标记以进行缺陷检测

Towards Reducing Data Acquisition and Labeling for Defect Detection  using Simulated Data

In the wood industry, logs are commonly quality screened by discrete X-ray
scans on a moving conveyor belt from a few source positions. Typically,
two-dimensional (2D) slice-wise measurements are obtained by a sequential
scanning geometry. Each 2D slice alone does not carry sufficient information
for a three-dimensional tomographic reconstruction in which biological features
of interest in the log are well preserved. In the present work, we propose a
learned iterative reconstruction method based on the Learned Primal-Dual neural
network, suited for sequential scanning geometries. Our method accumulates
information between neighbouring slices, instead of only accounting for single
slices during reconstruction. Our quantitative and qualitative evaluations with
as few as five source positions show that our method yields reconstructions of
logs that are sufficiently accurate to identify biological features like knots
(branches), heartwood and sapwood.

在木材工业中，通过在运动传送带上的几个源位置进行离散 X 射线扫描，通常可以对木材进行质量筛选。本文提出了一种基于学习的原始 - 对偶神经网络的迭代重建方法，适用于顺序扫描几何。我们的方法在重建过程中累积了相邻切片之间的信息，而不仅仅考虑单个切片。通过定量和定性评估，我们的方法在仅使用五个源位置时能够准确重建木材，并识别出节（枝）、心材和边材等生物特征。