Identifying Systematic Errors in Object Detectors with the SCROD Pipeline

• URL: http://arxiv.org/abs/2309.13489v1
• Date: Sat, 23 Sep 2023 22:41:08 GMT
• Title: Identifying Systematic Errors in Object Detectors with the SCROD Pipeline
• Authors: Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
• Abstract summary: The identification and removal of systematic errors in object detectors can be a prerequisite for their deployment in safety-critical applications. We overcome this limitation by generating synthetic images with fine-granular control. We propose a novel framework that combines the strengths of both approaches.
• Score: 46.52729366461028
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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