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Academic Journal of Computing & Information Science, 2022, 5(5); doi: 10.25236/AJCIS.2022.050501.

Road object recognition method based on improved YOLOv3


Yichi Zhang1, Boyu Hu2, Xinyi Yuan3, Yupeng Li4

Corresponding Author:
Yichi Zhang

1University of Nottingham, Ningbo, Zhejiang, China

2Beijing Information Science and Technology University, Beijing, China

3Xihua University, Chengdu, Sichuan, China

4The Woodlands, Mississauga, Ontario, Canada


Based on the emergence and development of autonomous driving technology, the identification of obstacles on the road is a very important and challenging task. And there are many difficulties in the realization of this task, for example, there are many types of road targets, and the scale span of the road obstacles is large. In view of these problems, this experiment proposes three improvement directions for the YOLOv3 algorithm to perform the task of road target prediction: one is to improve the up-sampling multiple to use more shallow spatial information to improve the accuracy of small target detection. The second is to change the way of feature fusion of the feature pyramid. Thirdly, the convergence direction of the model is changed by clustering learning. Experiments on the BDD100K data set show that the yolov3_10cls_tiny algorithm proposed in this paper has the best detection performance and better average accuracy than YOLOv3.


Object recognition, YOLOv3, Clustering learning

Cite This Paper

Yichi Zhang, Boyu Hu, Xinyi Yuan, Yupeng Li. Road object recognition method based on improved YOLOv3. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 5: 1-9. https://doi.org/10.25236/AJCIS.2022.050501.


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