TY - GEN
T1 - Small Traffic Sign Detector in Real-time Based on Improved YOLO-v4
AU - Yang, Tingting
AU - Tong, Chao
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2022
Y1 - 2022
N2 - As one of the indispensable parts of the smart transportation system, traffic sign detection is still an open challenge for the reason that traffic sign detection, one kind of small object detection, requires high precision, high reliability, and real-time speed. In this paper, a real-time detector for small traffic sign improved from YOLO-v4 is proposed. First, a transpose convolution layer is added into the backbone of YOLO-v4, named CSPDark-53, to get larger feature maps with richer semantic information of small objects. Secondly, aiming at fusing multi-scale feature maps more effectively, we present two novel attention modules, the upsampling attention module and the downsampling attention module, to calculate the channel weights and spatial masks to help the network focus on useful features and suppress invalid features. The experiments show our detector outperforms the compared models in Chinese Traffic sign dataset TT100k.
AB - As one of the indispensable parts of the smart transportation system, traffic sign detection is still an open challenge for the reason that traffic sign detection, one kind of small object detection, requires high precision, high reliability, and real-time speed. In this paper, a real-time detector for small traffic sign improved from YOLO-v4 is proposed. First, a transpose convolution layer is added into the backbone of YOLO-v4, named CSPDark-53, to get larger feature maps with richer semantic information of small objects. Secondly, aiming at fusing multi-scale feature maps more effectively, we present two novel attention modules, the upsampling attention module and the downsampling attention module, to calculate the channel weights and spatial masks to help the network focus on useful features and suppress invalid features. The experiments show our detector outperforms the compared models in Chinese Traffic sign dataset TT100k.
KW - YOLO-v4
KW - attention module
KW - small object detection
KW - traffic sign detecion
UR - https://www.scopus.com/pages/publications/85132383811
U2 - 10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00200
DO - 10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00200
M3 - 会议稿件
AN - SCOPUS:85132383811
T3 - 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021
SP - 1318
EP - 1324
BT - 2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021
Y2 - 20 December 2021 through 22 December 2021
ER -