复杂路面小尺度行人检测综述
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国家自然科学基金青年基金(61902301)


Review on Small-scale Pedestrian Detection Technology for Complex Pavement
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    摘要:

    行人检测技术是智能交通和智能车辆发展的一个重要方向, 同时也是道路安全的重要保障, 直接影响着车辆控制系统对路况的判断. 在实际应用场景中, 小尺度行人实例占比非常高, 但小尺度行人检测一直是行人检测任务中一个困难且具有挑战性的问题. 而当智能汽车处于复杂的交通环境中时, 小尺度行人的精准检测可以使控制系统有时间提前预警并及时避让, 对于保障汽车安全平稳行驶起重要作用. 随着深度学习的快速发展, 小尺度行人检测技术取得了突破性的进展, 目前该技术的发展处于快速发展时期. 为了进一步促进小尺度行人检测技术的发展, 本文对小尺度行人检测技术的最新方法进行了全面研究. 本文首先分析了小尺度行人检测面临的几大挑战, 并对目前最新的小尺度行人检测网络进行了归类和总结. 本文从多尺度表示、上下文信息、新的训练和分类策略、尺度感知和超分辨率5个方面对现有的深度学习方法进行了分析和讨论, 其中多尺度学习方法为当前处理小尺度行人检测的主流方法. 同时简要介绍了行人检测常用的评价指标和数据集, 并在Caltech等通用数据集上对一些主流方法进行了性能评价. 此外, 本文还对5类方法进行了总结和对比. 最后, 本文从多个方面提出了行人检测技术中亟待解决的问题和未来发展的方向和任务.

    Abstract:

    Pedestrian detection technology is an important research direction for the development of intelligent transportation and intelligent vehicles, and it is also an important guarantee for road safety, which directly affects the judgment of a vehicle control system on road conditions. In practical application scenarios, small-scale pedestrian instances account for a very high proportion, but small-scale pedestrian detection has always been a challenging problem in pedestrian detection tasks. When an intelligent vehicle is in a complex traffic environment, the precise detection of small-scale pedestrians can make the control system give a warning in advance and help avoid collision in time, which plays an important role in ensuring the safe and stable driving of the vehicle. With the rapid development of deep learning, groundbreaking progress has been made in the fast-growing small-scale pedestrian detection technology. To further promote the development of small-scale pedestrian detection technology, this study conducts comprehensive research on the latest methods of small-scale pedestrian detection technology. To start with, this study analyzes several challenges faced by small-scale pedestrian detection and classifies and summarizes the latest small-scale pedestrian detection networks. The existing deep learning methods are analyzed and discussed from five aspects, namely multi-scale representation, context information, new training and classification strategies, scale perception, and super-resolution. Among them, the multi-scale learning method is the mainstream of small-scale pedestrian detection. Meanwhile, we briefly introduce the commonly used evaluation indicators and datasets for pedestrian detection and evaluate the performance of some mainstream methods on general datasets such as Caltech. In addition, five methods are summarized and compared in this study. Finally, this study proposes the urgent problems to be solved in pedestrian detection technology and the direction and tasks of future development from multiple aspects.

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赵书,陈宁.复杂路面小尺度行人检测综述.计算机系统应用,2022,31(7):1-11

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  • 收稿日期:2021-09-23
  • 最后修改日期:2021-10-25
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  • 在线发布日期: 2022-05-31
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