people

Pedestrian safety is one primary concern in au-
tonomous driving. The under-representation of vulnerable groups
in today’s pedestrian datasets points to an urgent need for a
dataset of vulnerable road users. In order to help train com-
prehensive models and subsequently drive research to improve
the accuracy of vulnerable pedestrian identification, we first
introduce a new dataset for vulnerable pedestrian detection in
this paper: the BG Vulnerable Pedestrian (BGVP) dataset. The
dataset includes four classes, i.e., Children Without Disability,
Elderly without Disability, With Disability, and Non-Vulnerable.
This dataset consists of images collected from the public domain
and manually-annotated bounding boxes. In addition, on the
proposed dataset, we have trained and tested five classic or
state-of-the-art object detection models, i.e., YOLOv4, YOLOv5,
YOLOX, Faster R-CNN, and EfficientDet. Our results indicate
that YOLOX and YOLOv4 perform the best on our dataset,
YOLOv4 scoring 0.7999 and YOLOX scoring 0.7779 on the
mAP 0.5 metric, while YOLOX outperforms YOLOv4 by 3.8
percent on the mAP 0.5:0.95 metric. Generally speaking, all
five detectors do well predicting the With Disability class and
perform poorly in the Elderly Without Disability class. YOLOX
consistently outperforms all other detectors on the mAP (0.5:0.95)
per class metric, obtaining 0.5644, 0.5242, 0.4781, and 0.6796 for
Children Without Disability, Elderly Without Disability, Non-
vulnerable, and With Disability, respectively.

搜集汇总

背景与挑战

背景概述

该数据集名为BG Vulnerable Pedestrian (BGVP)，专注于自动驾驶中的弱势行人检测，包含儿童无残疾、老年无残疾、有残疾和非弱势四类图像，并提供了手动标注的边界框。数据集已用于训练和评估多种目标检测模型，如YOLOv4和YOLOX，以提升弱势行人识别的准确性。