TraCon: A novel dataset for real-time traffic cones detection using deep learning

Agapi Davradou; Anastasios Doulamis; Dimitris Kalogeras; Eftychios Protopapadakis; Eleni Eirini Karolou; Iason Katsamenis; Nikolaos Doulamis

TraCon: A novel dataset for real-time traffic cones detection using deep learning

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arxiv 2205.11830 v1 pith:SNXCTBCI submitted 2022-05-24 cs.CV

Iason Katsamenis , Eleni Eirini Karolou , Agapi Davradou , Eftychios Protopapadakis , Anastasios Doulamis , Nikolaos Doulamis , Dimitris Kalogeras This is my paper · ORCID

classification cs.CV

keywords detectiontrafficbeenconesdatasetobjectroadyolov5

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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.

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