Computer vision (CV) pipelines are typically evaluated on datasets processed by image signal processing (ISP) pipelines even though, for resource-constrained applications, an important research goal is to avoid as many ISP steps as possible. In particular, most CV datasets consist of global shutter (GS) images even though most cameras today use a rolling shutter (RS). This paper studies the impact of different shutter mechanisms on machine learning (ML) object detection models on a synthetic dataset that we generate using the advanced simulation capabilities of Unreal Engine 5 (UE5). In particular, we train and evaluate mainstream detection models with our synthetically-generated paired GS and RS datasets to ascertain whether there exists a significant difference in detection accuracy between these two shutter modalities, especially when capturing low-speed objects (e.g., pedestrians). The results of this emulation framework indicate the performance between them are remarkably congruent for coarse-grained detection (mean average precision (mAP) for IOU=0.5), but have significant differences for fine-grained measures of detection accuracy (mAP for IOU=0.5:0.95). This implies that ML pipelines might not need explicit correction for RS for many object detection applications, but mitigating RS effects in ISP-less ML pipelines that target fine-grained location of the objects may need additional research.

通过使用Unreal Engine 5（UE5）的先进模拟能力生成合成数据集，本论文研究了不同快门机制对机器学习（ML）目标检测模型的影响，结果表明在IOU=0.5时，这两种快门模态之间的性能相当，在IOU=0.5:0.95范围内的检测准确度存在显著差异，这意味着对于许多目标检测应用程序，ML流水线可能不需要对RS进行显式校正，但针对目标的细粒度位置的ISP-less ML流水线可能需要额外的研究。

合成数据集分析：不同快门类型下的行人检测