TY - GEN
T1 - Research on Vehicle and Pedestrian Detection Algorithm in Foggy Weather Based on Improved YOLOv5s
AU - Liu, Luohang
AU - Wu, Zhicheng
AU - Wang, Xinyu
AU - Xie, Panpan
AU - Ren, Hongbin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Aiming at the problem of the bad performance of current automatic driving perception algorithm in foggy weather, this paper proposes a vehicle and pedestrian detection algorithm in foggy weather based on improved YOLOv5s. Firstly, based on Cityscapes dataset, artificially generate foggy images of three concentrations to form Foggy Cityscapes dataset. Secondly, C3STR structure is introduced into the backbone network to better improve the extraction ability of model for global features. Thirdly, de-weighted BiFPN is introduced into the neck network, adding a jump connection from backbone directly to PAN, strengthening the feature fusion and reducing the loss of feature information. Finally, EIoU is used in the boundary box loss function, which improves the regression precision and accelerates the convergence speed. The experiment results show that the algorithm proposed in this paper achieves good results in both synthetic foggy image dataset and real foggy image dataset, and meets the real-Time requirement, which has certain practicability.
AB - Aiming at the problem of the bad performance of current automatic driving perception algorithm in foggy weather, this paper proposes a vehicle and pedestrian detection algorithm in foggy weather based on improved YOLOv5s. Firstly, based on Cityscapes dataset, artificially generate foggy images of three concentrations to form Foggy Cityscapes dataset. Secondly, C3STR structure is introduced into the backbone network to better improve the extraction ability of model for global features. Thirdly, de-weighted BiFPN is introduced into the neck network, adding a jump connection from backbone directly to PAN, strengthening the feature fusion and reducing the loss of feature information. Finally, EIoU is used in the boundary box loss function, which improves the regression precision and accelerates the convergence speed. The experiment results show that the algorithm proposed in this paper achieves good results in both synthetic foggy image dataset and real foggy image dataset, and meets the real-Time requirement, which has certain practicability.
KW - BiFPN
KW - EIoU
KW - Swin-Transformer
KW - YOLOv5s
KW - object detection in foggy weather
UR - http://www.scopus.com/inward/record.url?scp=85175064471&partnerID=8YFLogxK
U2 - 10.1109/IRCE59430.2023.10255068
DO - 10.1109/IRCE59430.2023.10255068
M3 - Conference contribution
AN - SCOPUS:85175064471
T3 - 6th International Conference on Intelligent Robotics and Control Engineering, IRCE 2023
SP - 230
EP - 235
BT - 6th International Conference on Intelligent Robotics and Control Engineering, IRCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Intelligent Robotics and Control Engineering, IRCE 2023
Y2 - 4 August 2023 through 6 August 2023
ER -