Chinese Journal of Liquid Crystals and Displays, Volume. 37, Issue 9, 1228(2022)
Real-time detection model of highway vehicle based on YOLOv5s
[5] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv, 1804. 02767(2018).
[6] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv preprint arXiv, 2020.
[7] WANG Y X, SONG H S, LIANG H X et al. Highway vehicle object detection based on improved YOLOv4 method[J]. Computer Engineering and Applications, 57, 218-226(2021).
[17] JETLEY S, LORD N A, LEE N et al. Learn to pay attention[C](2018).
[23] ZHANG B L, QIN H R, JIANG S et al. A method of vehicle detection at night based on RetinaNet and optimized loss functions[J]. Automotive Engineering, 43, 1195-1202(2021).
[26] SHI J, CHENG Q, JIN L S et al. Fine-grained vehicle detection and classification model for video structuring description[J]. Automotive Engineering, 43, 1427-1434(2021).
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Yuan-feng LIU, Hai-jun JI, Li-bo LIU. Real-time detection model of highway vehicle based on YOLOv5s[J]. Chinese Journal of Liquid Crystals and Displays, 2022, 37(9): 1228
Category: Research Articles
Received: Jan. 24, 2022
Accepted: --
Published Online: Sep. 19, 2022
The Author Email: Li-bo LIU (liulib@163.com)