论文标题
Tracon：使用深度学习的实时交通锥检测的新颖数据集
TraCon: A novel dataset for real-time traffic cones detection using deep learning
论文作者
论文摘要
在道路场景中的物体检测领域已经取得了很大的进展。但是，它主要集中在车辆和行人上。为此，我们调查了交通锥检测，这是对道路效应和维护至关重要的对象类别。在这项工作中，采用了Yolov5算法，以找到有效且快速检测交通锥的解决方案。 Yolov5的得分高达91.31％，可以达到高检测精度。提出的方法被应用于从各种来源收集的RGB道路图像数据集。
Substantial progress has been made in the field of object detection in road scenes. However, it is mainly focused on vehicles and pedestrians. To this end, we investigate traffic cone detection, an object category crucial for road effects and maintenance. In this work, the YOLOv5 algorithm is employed, in order to find a solution for the efficient and fast detection of traffic cones. The YOLOv5 can achieve a high detection accuracy with the score of IoU up to 91.31%. The proposed method is been applied to an RGB roadwork image dataset, collected from various sources.
