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

arXiv: https://arxiv.org/abs/2205.11830 · HTML: https://mlsanity.com/paper/2205.11830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 5 of 5)

- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Real-time 3D Traffic Cone Detection for Autonomous Driving](https://mlsanity.com/paper/1902.02394/) (2019)
- [Robotic Maintenance of Road Infrastructures: The HERON Project](https://mlsanity.com/paper/2205.04164/) (2022)
- [Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation](https://mlsanity.com/paper/2008.05204/) (2020)

## Built on by (most cited first, 4 of 4)

- [Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localization](https://mlsanity.com/paper/2607.04330/) (2026)
- [Situation Awareness for Automated Surgical Check-listing in AI-Assisted Operating Room](https://mlsanity.com/paper/2209.05056/) (2022)
- [Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning](https://mlsanity.com/paper/2603.09255/) (2026)
- [SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks](https://mlsanity.com/paper/2608.10289/) (2026)

## Abstract (arXiv)

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.