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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Object Detection With Deep Learning: A Review
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Object Detection With Deep Learning: A Review

机译：深度学习中的对象检测：回顾

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摘要

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.
机译：由于物体检测与视频分析和图像理解之间有着密切的关系，因此近年来引起了很多研究关注。传统的物体检测方法建立在手工制作的特征和浅层可训练的体系结构上。通过构建将来自对象检测器和场景分类器的多个低级图像特征与高级上下文相结合的复杂集合，它们的性能容易停滞。随着深度学习的飞速发展，引入了能够学习语义，高级，更深入的功能的更强大的工具，以解决传统体系结构中存在的问题。这些模型在网络体系结构，训练策略和优化功能方面的行为有所不同。在本文中，我们对基于深度学习的对象检测框架进行了概述。我们的回顾首先简要介绍了深度学习的历史及其代表工具，即卷积神经网络。然后，我们将重点介绍典型的通用对象检测体系结构以及一些改进和有用的技巧，以进一步提高检测性能。由于不同的特定检测任务表现出不同的特性，因此我们还将简要调查几个特定任务，包括显着物体检测，面部检测和行人检测。还提供了实验分析以比较各种方法并得出一些有意义的结论。最后，提供了一些有希望的方向和任务，以作为将来在对象检测和相关的基于神经网络的学习系统中工作的指南。

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