mlsanityData to 14 Oct 2026

Vision-based Warning System for Maintenance Personnel on Short-Term Roadwork Site

arXiv 2210.01689 ↗First posted 4 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

We propose a vision-based warning system for the maintenance personnel working on short-term construction sites. Traditional solutions use passive protection, like setting up traffic cones, safety beacons, or even nothing. However, such methods cannot function as physical safety barriers to separate working areas from used lanes. In contrast, our system provides active protection, leveraging acoustic and visual warning signals to help road workers be cautious of approaching vehicles before they pass the working area. To decrease too many warnings to relieve a disturbance of road workers, we implemented our traffic flow check algorithm, by which about 80% of the useless notices can be filtered. We conduct the evaluations in laboratory conditions and the real world, proving our system's applicability and reliability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 20 references that are in the map. First posted on arXiv 4 Oct 2022.

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8 of 20 references.

References · 8

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2017Mask R-CNNcited by 7,277
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  4. 2018YOLOv3: An Incremental Improvementcited by 2,659
  5. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  6. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
  7. 2014Multiple Object Tracking: A Literature Reviewcited by 166
  8. 2020An analysis of the transfer learning of convolutional neural networks for artistic imagescited by 11

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