Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

中文翻译：

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复杂场景下智能车辆的行人检测算法。

行人检测是智能车辆发展的重要方面。针对传统行人检测容易受到环境因素影响，无法实时满足精度要求的问题，本研究提出了一种复杂场景下智能车辆的行人检测算法。YOLOv3是目前基于深度学习的目标检测算法之一，具有良好的性能。在本文中，首先阐述并分析了YOLOv3的基本原理，以确定其在行人检测中的局限性。然后，在原始YOLOv3网络模型的基础上，进行了许多改进，包括修改网格像元大小，采用改进的k均值聚类算法，改进基于接收场的多尺度边界框预测，并使用Soft-NMS算法。最后，基于INRIA人员和PASCAL VOC 2012数据集，进行了行人检测实验，以测试该算法在各种复杂场景下的性能。实验结果表明，平均平均精度（mAP）值达到90.42％，每帧的平均处理时间为9.6 ms。与其他检测算法相比，该算法在实时性，实时性，复杂场景下均具有良好的鲁棒性和抗干扰能力，具有较强的泛化能力，较高的网络稳定性和检测精度和检测速度。这些改进对于保护行人的道路安全和减少交通事故具有重要意义，