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

• URL: http://arxiv.org/abs/2205.11830v1
• Date: Tue, 24 May 2022 06:51:58 GMT
• Title: TraCon: A novel dataset for real-time traffic cones detection using deep learning
• Authors: Iason Katsamenis, Eleni Eirini Karolou, Agapi Davradou, Eftychios Protopapadakis, Anastasios Doulamis, Nikolaos Doulamis, Dimitris Kalogeras
• Abstract summary: 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%.
• Score: 7.759841699582662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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