  Pedestrian safety is one primary concern in autonomous 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 comprehensive 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.
Our dataset and codes are available at https://github.com/devvansh1997/BGVP.
