A Deep Reinforcement Learning Object Detection Model Based on Improved Convolutional Attention Mechanism

Deep reinforcement learning is the combination of reinforcement learning and deep learning. Reinforcement learning is used to define the problem itself and optimize the objective function, and deep learning is used to solve the problems such as state representation and policy expression. The advantage of attention mechanism is the ability to focus on the important parts of the input information. Object detection is an important branch of computer vision. The detection accuracy is constantly improving, but it is still far from meeting the requirements of human beings. One of the main reasons is that the model lacks the ability to express the target to be detected. In order to solve the problem of low detection accuracy and slow speed in deep reinforcement learning object detection model, this paper proposes a deep reinforcement learning object detection model based on improved convolutional attention mechanism. Through experiments, it is found that the model has better performance in the detection task.