Adjusting camera exposure in arbitrary lighting conditions is the first step to ensure the functionality of computer vision applications. Poorly adjusted camera exposure often leads to critical failure and performance degradation. Traditional camera exposure control methods require multiple convergence steps and time-consuming processes, making them unsuitable for dynamic lighting conditions. In this paper, we propose a new camera exposure control framework that rapidly controls camera exposure while performing real-time processing by exploiting deep reinforcement learning. The proposed framework consists of four contributions: 1) a simplified training ground to simulate real-world's diverse and dynamic lighting changes, 2) flickering and image attribute-aware reward design, along with lightweight state design for real-time processing, 3) a static-to-dynamic lighting curriculum to gradually improve the agent's exposure-adjusting capability, and 4) domain randomization techniques to alleviate the limitation of the training ground and achieve seamless generalization in the wild.As a result, our proposed method rapidly reaches a desired exposure level within five steps with real-time processing (1 ms). Also, the acquired images are well-exposed and show superiority in various computer vision tasks, such as feature extraction and object detection.

翻译：在各种光照条件下调整相机曝光是确保计算机视觉应用功能的首要步骤。曝光调整不当常常导致关键性故障和性能下降。传统相机曝光控制方法需要多次收敛步骤和耗时流程，难以适应动态光照条件。本文提出一种新的相机曝光控制框架，通过利用深度强化学习在实时处理的同时快速控制相机曝光。该框架包含四项贡献：1) 模拟真实世界多样动态光照变化的简化训练环境，2) 闪烁与图像属性感知的奖励设计及面向实时处理的轻量级状态设计，3) 从静态到动态光照的课程学习策略以逐步提升智能体的曝光调整能力，4) 缓解训练环境局限性并实现无缝野外泛化的域随机化技术。实验表明，所提方法能在五个步骤内（实时处理耗时1毫秒）快速达到期望曝光水平，且获取的图像曝光良好，在特征提取、目标检测等多项计算机视觉任务中展现优越性能。