Hierarchical Image Object Search Based on Deep Reinforcement Learning

Object detection technology occupies a pivotal position in the field of modern computer vision research, its purpose is to accurately locate the object human beings are looking for in the image and classify the object. With the development of deep learning technology, convolutional neural networks are widely used because of their outstanding performance in feature extraction, which greatly improves the speed and accuracy of object detection. In recent years, reinforcement learning technology has emerged in the field of artificial intelligence, showing excellent decision-making ability to deal with problems. In order to combine the perception ability of deep learning technology with the decision-making ability of reinforcement learning technology, this paper incorporate reinforcement learning into the convolutional neural network, and propose a hierarchical deep reinforcement learning object detection model.