  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.
