Autonomous driving systems require many images for analyzing the surrounding environment. However, there is fewer data protection for private information among these captured images, such as pedestrian faces or vehicle license plates, which has become a significant issue. In this paper, in response to the call for data security laws and regulations and based on the advantages of large Field of View(FoV) of the fisheye camera, we build the first Autopilot Desensitization Dataset, called ADD, and formulate the first deep-learning-based image desensitization framework, to promote the study of image desensitization in autonomous driving scenarios. The compiled dataset consists of 650K images, including different face and vehicle license plate information captured by the surround-view fisheye camera. It covers various autonomous driving scenarios, including diverse facial characteristics and license plate colors. Then, we propose an efficient multitask desensitization network called DesCenterNet as a benchmark on the ADD dataset, which can perform face and vehicle license plate detection and desensitization tasks. Based on ADD, we further provide an evaluation criterion for desensitization performance, and extensive comparison experiments have verified the effectiveness and superiority of our method on image desensitization.

翻译：自动驾驶系统需要大量图像来分析周围环境。然而，这些捕获图像中的隐私信息（如行人面部或车辆车牌）缺乏足够的数据保护，已成为一个重大问题。本文响应数据安全法律法规的号召，基于鱼眼相机大视场角（FoV）的优势，构建了首个自动驾驶脱敏数据集（ADD），并提出了首个基于深度学习的图像脱敏框架，以促进自动驾驶场景中图像脱敏的研究。该数据集包含650K张图像，涵盖由环视鱼眼相机捕获的不同人脸和车辆车牌信息，涉及多种自动驾驶场景，包括多样化的面部特征和车牌颜色。随后，我们提出了一种名为DesCenterNet的高效多任务脱敏网络作为ADD数据集的基准，该网络可执行人脸和车辆车牌的检测与脱敏任务。基于ADD，我们进一步提供了脱敏性能的评估标准，并通过广泛的对比实验验证了本方法在图像脱敏上的有效性和优越性。