Road Damage Detection Using YOLO with Smartphone Images

被引：30

作者：

Jeong, Dongjun ^{[1
]}

机构：

[1] Univ Southern Denmark, SDU Robot, Campusvej 55, DK-5230 Odense, Denmark

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2020年

关键词：

Deep Learning; Road Damage Dataset; YOLO;

D O I：

10.1109/BigData50022.2020.9377847

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deep learning-based technology is a good key to unlock the object detection tasks in our real world. By using deep neural networks, we could break a problem that is dangerous and very time-consuming but has to be done every day like detecting the road state. This paper describes the solution using YOLO to detect the various types of road damage in the IEEE BigData Cup Challenge 2020. Our YOLOv5x based-solution is light-weight and fast, even it has good accuracy. We achieved an F1 score of 0.58 using our ensemble model with TTA, and it could be an adequate candidate for detecting real road damage in real-time.

引用

页码：5559 / 5562

页数：4

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