Identifying Systematic Errors in Object Detectors with the SCROD
  Pipeline
Valentyn Boreiko Matthias Hein Jan Hendrik Metzen
The identification and removal of systematic errors in object detectors can
be a prerequisite for their deployment in safety-critical applications like
automated driving and robotics. Such systematic errors can for instance occur
under very specific object poses (location, scale, orientation), object
colors/textures, and backgrounds. Real images alone are unlikely to cover all
relevant combinations. We overcome this limitation by generating synthetic
images with fine-granular control. While generating synthetic images with
physical simulators and hand-designed 3D assets allows fine-grained control
over generated images, this approach is resource-intensive and has limited
scalability. In contrast, using generative models is more scalable but less
reliable in terms of fine-grained control. In this paper, we propose a novel
framework that combines the strengths of both approaches. Our meticulously
designed pipeline along with custom models enables us to generate street scenes
with fine-grained control in a fully automated and scalable manner. Moreover,
our framework introduces an evaluation setting that can serve as a benchmark
for similar pipelines. This evaluation setting will contribute to advancing the
field and promoting standardized testing procedures.