Abstract
Abstract 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.
Highlights
When observing a picture, humans can immediately know the location and category of the object in the image, and can get the information without even thinking too much
The positioning and retrieval of images will be affected by two aspects, one is the content of the image, and the other is the pros and cons of the algorithm
The first is that the background and light when taking pictures will affect the quality of the image, resulting in a decrease in the accuracy of object detection
Summary
Humans can immediately know the location and category of the object in the image, and can get the information without even thinking too much. How to design an algorithm that can satisfy accurate positioning and continuously improve the object positioning speed is the key to research These pictures are data collections which are composed of binary digits, and the things behind the data cannot be imagined by computers. Reinforcement learning is an important field in machine learning It constructs a Markov Decision Process and simulates human thinking to teach agents how to make actions that can obtain high reward values in the environment, and find the best strategy to solve the problem in such constant interaction. Based on this idea, this paper use reinforcement learning technology to simulate the human visual attention mechanism. The object of image positioning and classification can be achieved
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