TY  - JOUR
PY  - 2023//
TI  - HRBUST-LLPED: a benchmark dataset for wearable low-light pedestrian detection
JO  - Micromachines (Basel)
A1  - Li, Tianlin
A1  - Sun, Guanglu
A1  - Yu, Linsen
A1  - Zhou, Kai
SP  - e2164
EP  - e2164
VL  - 14
IS  - 12
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2072-666X
UR  - http://dx.doi.org/10.3390/mi14122164
ID  - ref1
ER  -