State-of-the-art object detection methods based on deep learning achieved promising performances in recent years. However, the performances are limited by the passive nature of the traditional object recognition framework in ignoring the relationship between imaging configuration and recognition performance as well as the importance of recognition performance feedback for improving image quality. To address the above limitations, an active object recognition method based on reinforcement learning is proposed in this paper by taking adaptive brightness adjustment as an example. Progressive brightness adjustment strategy is learned by maximizing recognition performance on reference high-quality training samples. With the help of active object recognition and brightness adjustment strategy, low-quality images can be converted into high-quality images, and overall performances are improved without retraining the detector.