人工智能系统中依赖图像质量的退化

中文摘要 AI 辅助

感知是神经网络优于传统算法的主要应用领域之一，如自动驾驶人工智能系统可基于图像数据检测行人并避让。但这类人工智能系统面临的重大挑战是其输出严重依赖输入图像质量，图像质量差时难以进行准确预测，还会出现各种与系统可信度相关的错误。本文旨在通过基于估计图像质量降低网络置信阈值建立故障退化系统，使其在不确定情况下更谨慎地检测物体。还提出一种通过归一化流将输入图像与训练数据比较来估计图像质量的新方法，并将该方法应用于先进目标检测进行实验。总之，提出了一种自动驾驶中基于人工智能系统的设计策略，可处理低质量输入数据，增强对基于人工智能系统的信任并增加人工智能组件的供应。

英文摘要

Perception is one of the primary applications where neural networks outperform conventional algorithms. One example is AI systems for automated driving, which can detect pedestrians based on image data and avoid them accordingly. A substantial challenge with these AI systems is that their output depends heavily on the quality of the input images. For example, if an image is of inferior quality due to heavy contamination, such as noise or darkness, accurate predictions are hardly feasible. Additionally, various types of errors can occur, each with varying relevance to the trustworthiness of the underlying AI system. In particular, it may be more critical not to detect an existing person than to detect a person where there is none. Therefore, we want to show that we can still avoid the most critical errors in situations of inferior image quality. To achieve this, we aim to establish a fail-degraded system by lowering the network's confidence threshold based on the estimated image quality, enabling it to detect objects more cautiously in uncertain situations. Additionally, we present a novel method for estimating the quality of incoming images by comparing them to the training data using normalizing flows. We will also conduct experiments applying our method to state-of-the-art object detection. In summary, we will present a design strategy for AI-based systems in automated driving that can deal with poor-quality input data without resorting to fallback solutions. Such measures enhance trust in AI-based systems and lead to an increased provision of the AI component.

发表机构

• German Aerospace Center (DLR)（德国航空航天中心（DLR））

机构由 AI 辅助整理，请以论文原文为准。