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Deep Reinforcement Learning for Object Detection with the Updatable Target Network

We propose a reinforcement learning method for setting up the game of detecting objects within an image. Unlike some traditional image detection methods, which produce a large number of candidate boxes to detect objects, our approach allows an agent to use a small set of image locations to detect a visual object effectively. We train the agent to identify the useless information in the four edges of the image and discard them by representing predefined candidate areas in a tree-like hierarchy. Such a series of procedures that build the game environment is designed to detect locations of target objects. We also show that the updatable target network can make the agent reach stability faster and improve training results prominently, which utilizes good samples. Extensive comparison experiments on the benchmark dataset of Pascal VOC verify the outperformance of the proposed method.