Are we heading for an iceberg with the current testing of machine vision?
This work delves into the landscape of Machine Vision (MV) testing, which is
heavily required in Highly Automated Driving (HAD) systems. Utilizing the
metaphorical notion of navigating towards an iceberg, we discuss the potential
shortcomings concealed within current testing strategies. We emphasize the
urgent need for a deeper understanding of how to deal with the opaque functions
of MV in development processes. As overlooked considerations can cost lives.
Our main contribution is the hierarchical level model, which we call
Granularity Grades. The model encourages a refined exploration of the
multi-scaled depths of understanding about the circumstances of environments in
which MV is intended to operate. This model aims to provide a holistic overview
of all entities that may impact MV functions, ranging from relations of
individual entities like object attributes to entire environmental scenes. The
application of our model delivers a structured exploration of entities in a
specific domain, their relationships and assigning results of a MV-under-test
to construct an entity-relationship graph. Through clustering patterns of
relations in the graph general MV deficits are arguable. In Summary, our work
contributes to a more nuanced and systematized identification of deficits of a
MV test object in correlation to holistic circumstances in HAD operating
domains.

当前的机器视觉测试是否在走向一次巨大灾难？本研究探讨了机器视觉测试的现状与潜在缺陷，并提出了一个层级模型 ——Granularity Grades，该模型致力于对环境情境中各实体的综合认知。通过构建实体关系图，并对图中的关系模式进行聚类分析，我们可以系统地识别机器视觉测试对象的缺陷，并与高度自动化驾驶系统中的整体情境进行关联。