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.

基于全景鱼眼相机优势，建立了第一个Autopilot Desensitization Dataset（ADD）并构建了第一个基于深度学习的图像去敏感化框架，促进了自动驾驶场景下图像去敏感化的研究。数据集包含65万张图像，包括全景鱼眼相机捕获的各种面部特征和车牌信息，并提供了评估标准和比较实验证明了我们方法在图像去敏感化上的有效性和优越性。

ADD：自动驾驶中的自动去敏感化鱼眼数据集