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.

中文翻译：

---

基于深度学习的智能驾驶行人检测综述


行人检测是物体检测的一个具体应用。与一般的物体检测相比，它表现出相似性和独特性。此外，它在智能驾驶、安全监控等领域也具有重要的应用价值。近年来，随着深度学习的快速发展，行人检测技术也取得了长足的进步。然而，它与人类的认知仍然存在巨大差距。同时，还存在很多问题，还有很大的研究空间。对于行人检测在智能驾驶技术中的应用，必须保证其实时性。此外，在保证检测精度的同时，还需要减轻模型重量。本文首先简单介绍了行人检测的发展过程，然后重点总结了深度学习阶段行人检测技术的研究成果。随后，通过总结行人检测数据集和评价标准，分析了当前行人检测发展的核心问题。最后在论文的最后阐述了行人检测技术下一步可能的发展方向。