DRL-AE

Abstract

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 quantity-aware reward design, along with lightweight state-action design for real-time processing, 

3) a static-to-dynamic lighting curriculum learning method to gradually improve the agent's exposure-adjusting capability, and 

4) domain randomization techniques to exceed the limitation of the training ground and achieve seamless generalization in the wild.

As a result, our proposed method instantly reaches the 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.