HRBUST-LLPED: A Benchmark Dataset for Wearable Low-Light Pedestrian Detection

Author:

Li Tianlin1,Sun Guanglu1,Yu Linsen1,Zhou Kai1ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin University of Science and Technology, No. 52 Xuefu Road, Nangang District, Harbin 150080, China

Abstract

Detecting pedestrians in low-light conditions is challenging, especially in the context of wearable platforms. Infrared cameras have been employed to enhance detection capabilities, whereas low-light cameras capture the more intricate features of pedestrians. With this in mind, we introduce a low-light pedestrian detection (called HRBUST-LLPED) dataset by capturing pedestrian data on campus using wearable low-light cameras. Most of the data were gathered under starlight-level illumination. Our dataset annotates 32,148 pedestrian instances in 4269 keyframes. The pedestrian density reaches high values with more than seven people per image. We provide four lightweight, low-light pedestrian detection models based on advanced YOLOv5 and YOLOv8. By training the models on public datasets and fine-tuning them on the HRBUST-LLPED dataset, our model obtained 69.90% in terms of AP@0.5:0.95 and 1.6 ms for the inference time. The experiments demonstrate that our research can assist in advancing pedestrian detection research by using low-light cameras in wearable devices.

Funder

Key Research and Development Project of Heilongjiang Province

2020 Heilongjiang Province Higher Education Teaching Reform Project

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering

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