Precise perception of the environment is essential in highly automated driving systems, which rely on machine learning tasks such as object detection and segmentation. Compression of sensor data is commonly used for data handling, while virtualization is used for hardware-in-the-loop validation. Both methods can alter sensor data and degrade model performance. This necessitates a systematic approach to quantifying image validity. This paper presents a four-step framework to evaluate the impact of image modifications on machine learning tasks. First, a dataset with modified images is prepared to ensure one-to-one matching image pairs, enabling measurement of deviations resulting from compression and virtualization. Second, image deviations are quantified by comparing the effects of compression and virtualization against original camera-based sensor data. Third, the performance of state-of-the-art object detection models is analyzed to determine how altered input data affects perception tasks, including bounding box accuracy and reliability. Finally, a correlation analysis is performed to identify relationships between image quality and model performance. As a result, the LPIPS metric achieves the highest correlation between image deviation and machine learning performance across all evaluated machine learning tasks.

翻译：在高度自动化的驾驶系统中，精确的环境感知至关重要，这类系统依赖于目标检测与分割等机器学习任务。传感器数据压缩通常用于数据处理，而虚拟化则用于硬件在环验证。这两种方法都可能改变传感器数据并降低模型性能，因此需要一种系统化的方法来量化图像有效性。本文提出一个四步框架来评估图像修改对机器学习任务的影响。首先，准备包含修改后图像的数据集，确保形成一一对应的图像对，从而能够测量由压缩和虚拟化产生的偏差。其次，通过将压缩和虚拟化的效果与原始基于摄像头的传感器数据进行比较，量化图像偏差。第三，分析先进目标检测模型的性能，以确定改变后的输入数据如何影响感知任务，包括边界框精度和可靠性。最后，进行相关性分析以识别图像质量与模型性能之间的关系。结果表明，在所有评估的机器学习任务中，LPIPS指标在图像偏差与机器学习性能之间实现了最高的相关性。