Nowadays, utilizing Advanced Driver-Assistance Systems (ADAS) has absorbed a huge interest as a potential solution for reducing road traffic issues. Despite recent technological advances in such systems, there are still many inquiries that need to be overcome. For instance, ADAS requires accurate and real-time detection of pedestrians in various driving scenarios. To solve the mentioned problem, this paper aims to fine-tune the YOLOv5s framework for handling pedestrian detection challenges on the real-world instances of Caltech pedestrian dataset. We also introduce a developed toolbox for preparing training and test data and annotations of Caltech pedestrian dataset into the format recognizable by YOLOv5. Experimental results of utilizing our approach show that the mean Average Precision (mAP) of our fine-tuned model for pedestrian detection task is more than 91 percent when performing at the highest rate of 70 FPS. Moreover, the experiments on the Caltech pedestrian dataset samples have verified that our proposed approach is an effective and accurate method for pedestrian detection and can outperform other existing methodologies.

本文针对Advanced Driver-Assistance Systems (ADAS)中行人检测的挑战，使用YOLOv5s框架在Caltech行人数据集上进行fine-tune，并且开发了可用于准备数据和注释数据的工具箱。实验结果表明，使用作者提出的方法，行人检测任务的均值平均精度（mAP）可高达91％，且最高帧率可达70 FPS，是一种比其他现有技术更为有效和准确的方法。

自动驾驶车辆的稳健行人检测方法