We propose an evaluation framework that emulates poor image exposure conditions, low-range image sensors, lossy compression, as well as noise types which are common in robot vision. We present a rigorous evaluation of the robustness of several high-level image recognition models and investigate their performance under distinct image distortions. On one hand, F1 score shows that the majority of CNN models are slightly affected by mild exposure, strong compression, and Poisson Noise. On the other hand, there is a large decrease in precision and accuracy in extreme misexposure, impulse noise, or signal-dependent noise. Using the proposed framework, we obtain a detailed evaluation of a variety of traditional image distortions, typically found in robotics and automated systems pipelines, provides insights and guidance for further development. We propose a pipeline-based approach to mitigate the adverse effects of image distortions by including an image pre-processing step which intends to estimate the proper exposure and reduce noise artifacts. Moreover, we explore the impacts of the image distortions on the segmentation task, a task that plays a primary role in autonomous navigation, obstacle avoidance, object picking and other robotics tasks.

我们提出了一种评估框架，该框架可模拟不良的图像曝光条件，低距离图像传感器，有损压缩以及机器人视觉中常见的噪声类型。我们对几种高级图像识别模型的鲁棒性进行了严格的评估，并研究了它们在不同图像失真下的性能。一方面，F1分数表明，大多数CNN模型都受到轻度暴露，强压缩和泊松噪声的轻微影响。另一方面，极端误曝光，脉冲噪声或信号相关噪声的精度和准确度大大降低。使用提出的框架，我们可以获得各种传统图像失真的详细评估，这些图像失真通常出现在机器人技术和自动化系统管道中，为进一步开发提供了见识和指导。我们提出了一种基于流水线的方法，通过包括一个图像预处理步骤来减轻图像失真的不利影响，该步骤旨在估计适当的曝光并减少噪声伪像。此外，我们探索了图像变形对分割任务的影响，该任务在自主导航，避障，物体拾取和其他机器人任务中起主要作用。