TY - GEN
T1 - A Lightweight Fine-Grained VRU Detection Model for Roadside Units
AU - Shi, Jian
AU - Sun, Dongxian
AU - Zhang, Haodong
AU - Tan, Haiqiu
AU - Hu, Yaoguang
AU - Wang, Wuhong
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Object detection of vulnerable road users (VRU) under low computing resources of roadside units is one of the key technologies to achieve vehicle-infrastructure cooperative perception. In this paper, a lightweight fine-grained VRU detection model is proposed. Analyzing the existing complex traffic environment, the traditional definition of VRU is no longer applicable. Our work includes two parts: One is to redefine the fine-grained VRU and construct a new dataset. This task makes the perceptual information obtained by detection more comprehensive and accurate. Another is to optimize YOLOv4 by using the channel pruning method in model compression. The optimized model is 60% lighter than the original model. Under the limitation of low computing resources at the roadside units, the real-time detection of VRU is realized while ensuring a certain detection accuracy.
AB - Object detection of vulnerable road users (VRU) under low computing resources of roadside units is one of the key technologies to achieve vehicle-infrastructure cooperative perception. In this paper, a lightweight fine-grained VRU detection model is proposed. Analyzing the existing complex traffic environment, the traditional definition of VRU is no longer applicable. Our work includes two parts: One is to redefine the fine-grained VRU and construct a new dataset. This task makes the perceptual information obtained by detection more comprehensive and accurate. Another is to optimize YOLOv4 by using the channel pruning method in model compression. The optimized model is 60% lighter than the original model. Under the limitation of low computing resources at the roadside units, the real-time detection of VRU is realized while ensuring a certain detection accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85142675242&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-5615-7_21
DO - 10.1007/978-981-19-5615-7_21
M3 - Conference contribution
AN - SCOPUS:85142675242
SN - 9789811956140
T3 - Lecture Notes in Electrical Engineering
SP - 301
EP - 309
BT - Green Transportation and Low Carbon Mobility Safety - Proceedings of the 12th International Conference on Green Intelligent Transportation Systems and Safety
A2 - Wang, Wuhong
A2 - Wu, Jianping
A2 - Li, Ruimin
A2 - Jiang, Xiaobei
A2 - Zhang, Haodong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Green Intelligent Transportation Systems and Safety, 2021
Y2 - 17 November 2021 through 19 November 2021
ER -