In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users. However, data protection regulations require that individuals are anonymized in such datasets. In this work, we introduce a novel deep learning-based pipeline for face anonymization in the context of ITS. In contrast to related methods, we do not use generative adversarial networks (GANs) but build upon recent advances in diffusion models. We propose a two-stage method, which contains a face detection model followed by a latent diffusion model to generate realistic face in-paintings. To demonstrate the versatility of anonymized images, we train segmentation methods on anonymized data and evaluate them on non-anonymized data. Our experiment reveal that our pipeline is better suited to anonymize data for segmentation than naive methods and performes comparably with recent GAN-based methods. Moreover, face detectors achieve higher mAP scores for faces anonymized by our method compared to naive or recent GAN-based methods.

翻译：为保护行人、骑行者等弱势道路使用者（VRU），智能交通系统（ITS）必须对其进行准确识别。因此，用于训练ITS感知模型的数据集必须包含大量弱势道路使用者样本。然而，数据保护法规要求此类数据集中的人物必须经过匿名化处理。本研究提出一种基于深度学习的新型ITS人脸匿名化流程。与现有方法不同，我们未采用生成对抗网络（GAN），而是基于扩散模型领域的最新进展构建方案。我们提出两阶段方法：首先使用人脸检测模型，随后通过潜在扩散模型生成逼真的人脸修复图像。为验证匿名化图像的泛化能力，我们在匿名化数据上训练分割模型，并在非匿名化数据上进行评估。实验表明，本流程在分割任务中的匿名化效果优于朴素方法，且与近期基于GAN的方法性能相当。此外，经本方法匿名化的人脸在人脸检测任务中获得的mAP分数高于朴素方法与最新GAN方法。