TraCon: A novel dataset for real-time traffic cones detection using deep learning
This work addresses a specific need for road safety and maintenance by focusing on traffic cone detection, but it is incremental as it uses an existing method on new data.
The paper tackled the problem of detecting traffic cones in road scenes, which is understudied compared to vehicles and pedestrians, by applying the YOLOv5 algorithm to a new RGB dataset, achieving a high detection accuracy with an IoU score of 91.31%.
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.