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

2022/05/24 by Iason Katsamenis, Katsamenis, Iason, Eleni Eirini Karolou +11 · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Infrastructure Maintenance and Monitoring #cs.CV

paper · pdf · doi:10.48550/arxiv.2205.11830

10 pages, 5 figures

arxiv created 2022/05/24 · openalex publication_date 2022/05/24 · arxiv updated 2022/05/25 · openalex created_date 2022/05/27 · openalex updated_date 2026/07/28

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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• Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning

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