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          <h1 class="title is-1 publication-title">A sewer pipe crack simulator and data set</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a >Xing Wang</a><sup>1</sup>,</span>

            <span class="author-block">
              <a href="https://profiles.uts.edu.au/Raphael.Falque">Raphael Falque</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://www.westernsydney.edu.au/cfmat/people/karthick_thiyagarajan">Karthick Thiyagarajan</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="https://profiles.uts.edu.au/Alen.Alempijevic">Alen Alempijevic</a><sup>1</sup>,

            <span class="author-block">
              <a href="https://profiles.uts.edu.au/Sarath.Kodagoda">Sarath Kodagoda</a><sup>1</sup>,
            </span>


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          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>University of Technology Sydney</span>
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                  <span>The simulation project and dataset with a tutorial</span>
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      <img src="./images/simulatorv3.jpg"
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      <figcaption>The workflow of the simulator: CDM is used to blend background textures and crack masks and transform them into
materials applied to pipe models in Blender. Then, the resulting pipe models are used for simulation and data collection in
Unity. The simulated data is then used to train a YOLOv8n model. Without being retrained or fine-tuned using real data,
this model was also evaluated using real sewer images.</figcaption>

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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Inspection of highly valued wastewater pipe infrastructure is vital for cities worldwide, and many researchers
are trying to circumvent the limitations of traditional inspection methods using computer vision and robotic approaches.
However, these efforts are undermined by the limited access to
high-quality open-source data sets. The data sets should include
not only image sequences but also time stamps and Inertial
Measurement Unit (IMU) data, enabling researchers to simulate
a full pipe inspection by a robot. Bounding-box annotations
that indicate the location of the defects in the images are also
desirable. Limitations to current data sets are due to the lack
of some aforementioned information or their accessibility. This
paper presents our work on creating a sewer pipe simulator
and an example data set created using it. This simulator is
highly adaptable and editable, with all its assets accessible to
users. We also include an analysis of this data set using a You
Only Look Once (YOLO) v8 model in the paper. This simulator can serve
as the first step toward facilitating sewer defect detection using
simulated data, and it points out the possibility of creating
synthetic sewer data by leveraging the power of gaming engine and
generative models. Users can easily create their own data sets
by changing the background texture, using new crack masks,
adding additional objects or defective features, or altering the
movement of the camera by following our tutorial or code
comments.
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        This work will be presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024.
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  author={Le Gentil, Cedric and Falque, Raphael and Vidal-Calleja, Teresa},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  title={Real-Time Truly-Coupled Lidar-Inertial Motion Correction and Spatiotemporal Dynamic Object Detection},
  year={2024},
  volume={},
  number={},
}
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