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.

本文提出了一种基于深度强化学习的相机曝光控制框架，以实现实时处理并在不同动态光照条件下迅速控制相机曝光。该框架通过简化训练场景、奖励设计、状态设计和域随机化等方法，实现了在五个步骤内快速达到所需曝光水平，并在不同计算机视觉任务中表现出优越性。