Deep Learning for Generic Object Detection: A Survey
Deep Learning for Generic Object Detection: A Survey
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用于通用目标检测的深度学习：一项调查
DOI：
10.1007/s11263-019-01247-4
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发表时间：
2020-02-01
影响因子：
19.5
通讯作者：
Pietikainen, Matti
中科院分区：
文献类型：
--
作者：
Liu, Li;Ouyang, Wanli;Pietikainen, Matti
目标检测是计算机视觉中最基本和最具挑战性的问题之一，旨在从自然图像中的大量预定义类别中定位目标实例。深度学习技术已经成为直接从数据中学习特征表示的强大策略，并在通用对象检测领域取得了显着突破。鉴于这一快速发展的时期，本文的目标是全面调查深度学习技术在这一领域的最新成就。超过300个研究成果包括在这项调查中，涵盖了许多方面的通用对象检测：检测框架，对象特征表示，对象提案生成，上下文建模，训练策略和评估指标。我们通过确定未来研究的有希望的方向来完成调查。
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.