mlsanityData to 14 Oct 2026

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

arXiv 2205.11830 ↗First posted 24 May 2022cs.CVcited by 4

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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.

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.

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5 of 28 references.

References · 5

  1. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,368
  2. 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
  3. 2019Real-time 3D Traffic Cone Detection for Autonomous Drivingcited by 5
  4. 2022Robotic Maintenance of Road Infrastructures: The HERON Projectcited by 3
  5. 2020Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 3.

4 papers.

  1. 2026Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localizationcited by 1
  2. 2022Situation Awareness for Automated Surgical Check-listing in AI-Assisted Operating Roomcited by 0
  3. 2026Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloningcited by 0
  4. 2026SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networkscited by 0