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基于深度学习的智能驾驶行人检测综述。

A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning.

机构信息

School of Automobile, Chang'an University, Xi'an, Shaanxi 710064, China.

School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.

出版信息

Comput Intell Neurosci. 2021 Jul 20;2021:5410049. doi: 10.1155/2021/5410049. eCollection 2021.

Abstract

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

摘要

行人检测是目标检测的一个特定应用。与一般的目标检测相比,它表现出相似性和独特的特点。此外,它在智能驾驶和安全监控等领域具有重要的应用价值。近年来,随着深度学习的飞速发展,行人检测技术也取得了很大的进展。然而,它与人类感知之间仍然存在巨大的差距。同时,仍然存在许多问题,还有很大的研究空间。关于行人检测在智能驾驶技术中的应用,有必要确保其实时性能。此外,在确保检测精度的同时,还需要减轻模型的重量。本文首先简要描述了行人检测的发展过程,然后重点总结了行人检测技术在深度学习阶段的研究成果。随后,通过对行人检测数据集和评估标准的总结,分析了当前行人检测发展的核心问题。最后,在本文的结尾解释了行人检测技术未来可能的发展方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5d1/8318761/15cd4797f1cb/CIN2021-5410049.002.jpg

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