基于图像属性和深度学习的大规模物体检测研究与应用

项目名称： 基于图像属性和深度学习的大规模物体检测研究与应用

项目编号： No.61503366

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 罗平

作者单位： 中国科学院深圳先进技术研究院

项目金额： 21万元

中文摘要： 随着科技的高速发展，图像与视频数据持续增加并成为主要信息载体。如何准确高效的检测人脸、行人与常见物体已成为大数据时代一个迫切需要解决的问题。物体检测是抽取互联网有用信息、排除劣质信息的技术保障，是构建“平安城市”视频监控网络的基础，是下一代人工智能技术视觉系统如机器人和无人驾驶等的重要组成部分。大规模、非受控复杂图像与视频数据给传统物体检测技术带来巨大挑战。本项目使用图像属性和深度学习对物体检测流程进行统一建模与优化。拟解决的关键问题包括：1）复杂场景的大规模物体检测方法；2）弱监督与半监督深度学习方法；3）深度学习模型的时间效率优化。预期成果：算法方面，完成弱监督与半监督深度学习建模、优化和分析方法。并探索深度网络模型的压缩方法；应用方面，结合算法研究，搭建复杂场景下人脸、行人与常见物体检测流程。本项目的研究对推动物体检测技术在复杂环境下应用有着重要意义。

中文关键词： 多层次结构与深度学习网络；前馈神经网络；模型选择

英文摘要： Along with the development of modern science and technology, the number of images and videos increase rapidly. How to accurately and efficiently detect human faces, pedestrians, and objects becomes an important topic in the era of big data. Object detection is a fundamental technology which helps extract beneficial knowledge from internet, builds large scale surveillance system, and improves the next generation artificial intelligence such as robotics and drone. However, the conventional object detection methods face with new challenges in the big data era. For example, first, unconstrained and large scale image and video data often have large and complex variants, including crowd, occlusion, low resolution, and viewpoints. Second, the traditional object detection methods often used supervised learning, which needs a large number of annotated data. Nevertheless, data labeling in large dataset costs a lot of resources. Third, efficiency is an important issue in practical applications. To solve the above problems, this project combines attributes and deep learning to jointly model and train different key components of object detection. We are dealing with the following key issues: 1) large scale object detection in complex scenes; 2) weakly- and semi-supervised deep learning; and 3) improvement of the efficiency of deep models. This project adopts deep learning and improves large scale object detection, making it possible to be applied to object detection in real-world applications. In the aspect of algorithm, we will design weakly and semi-supervised deep learning methods and deep model compression. In the aspect of application, we will apply our deep models to large scale face detection, pedestrian detection, and object detection.

英文关键词： Deep Learning;Feed-Forward Neural Network;Model Selection