Jing Du’s research while affiliated with University of Florida and other places

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Publications (3)


Automating Building Damage Reconnaissance to Optimize Drone Mission Planning for Disaster Response
  • Article

May 2023

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87 Reads

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14 Citations

Journal of Computing in Civil Engineering

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Jing Du

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Rapid reconnaissance of building damage is critical for disaster response and recovery. Drones have been utilized to collect aerial images of affected areas in order to assess building damage. However, there are two challenges. First, processing many aerial images to detect and classify building damage based on a consistent standard remains laborious and complex, necessitating a new automated solution to achieve accurate building damage detection and classification. Second, drone operations during disaster response rely primarily on human operators' experience and seldom use the obtained building damage information to optimize drone mission planning. Therefore, this study proposes a new method, which automates building damage reconnaissance with drone mission planning for disaster response operations. Specifically, a deep learning method is developed to detect and classify building damages using a newly labeled dataset consisting of 24,496 distinct instances of building damage. This deep learning method is validated, achieving 71.9% mean average precision. In addition, building damage information is modeled and integrated into mission planning, in order to optimize drones' task assignments and route calculations. A tornado disaster in Tennessee is used as a case study to quantitatively evaluate this methodology. The present study concludes that optimal drone mission planning during disaster response can be augmented using accurate building damage information acquired from deep learning methods.


Seeing through Disaster Rubble in 3D with Ground-Penetrating Radar and Interactive Augmented Reality for Urban Search and Rescue

July 2022

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141 Reads

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18 Citations

Journal of Computing in Civil Engineering

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Long Chen

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Jing Du

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[...]

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First responders often lack information and visual clues regarding interior spaces in disaster rubble, preventing efficient, effective, and safe search and rescue for victims trapped in collapsed structures. Rapidly detecting and acquiring information about the voids in collapsed structures that could contain surviving victims is critical for urban search and rescue. However, reconstructing the buried voids in three dimensions (3D) and communicating the relevant information such as buried depth and void size to first responders remain significant challenges. In response, this study proposes a see-through technique by integrating ground-penetrating radar (GPR) with interactive augmented reality (AR). The contribution of this study is twofold. First, a new method is developed to process collected GPR data to reconstruct potential voids in disaster rubble in 3D and extract the buried depth and void size from the GPR data. The coordinates of void boundaries are extracted from multiple GPR scans to generate sparse point clouds. An improved alpha-shape method is exploited to reconstruct the 3D space beneath disaster rubble from the point clouds. Second, an interactive augmented reality interface is developed to enable first responders to visualize the voids in collapsed structures in 3D together with relevant information to assist urban search and rescue. The results from simulations and pilot experiments demonstrate the feasibility and potential of the proposed methods.


Human-in-the-Loop Robot-Augmented Intelligent System for Emergency Reconnaissance

May 2022

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25 Reads

This study proposes a human-in-the-loop robot-augmented intelligent system for emergency reconnaissance. The system improves situational awareness of first responders by providing robot-collected information through an intelligent and interactive interface. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) equipped with RGB-D camera, thermal camera, GPR, and LiDAR are deployed to acquire information on disaster sites. The collected sensor data are further processed to present actionable information to first responders, such as victim location, buried void shape, and accessible hole through an augmented reality interface. A virtual reality platform is used to validate and evaluate the proposed system. Participants are invited to complete a search and rescue mission with robot-augmented intelligent system to find survival victims under rubbles in the virtual environment. The experiments demonstrated that the proposed system reduces the searching time and false alarms, and increases the accuracy of detecting victims, which will change the search and rescue paradigm from an experience-based practice to an information-based one.

Citations (2)


... Additionally, a microservice architecture can enhance fleet management and emergency response by optimizing UAV resource allocation and synchronization. Deep learning integrated with task planning supports energy management, making UAVs more reliable for ice detection tasks in adverse conditions [169,170]. ...

Reference:

Investigation into UAV Applications for Environmental Ice Detection and De-Icing Technology
Automating Building Damage Reconnaissance to Optimize Drone Mission Planning for Disaster Response
  • Citing Article
  • May 2023

Journal of Computing in Civil Engineering

... This approach facilitates the training of AI models that are not only adept at recognizing human forms in clear view, but are also capable of inferring the presence of individuals in less than optimal visibility conditions. Such abilities are necessary for quickly finding emergency areas and finding survivors who need help right away, which makes SAR missions much more effective [2,8]. Through this comprehensive paper, we delineate the process undertaken to create our novel synthesized dataset (Fig. 1), emphasizing its designed complexity and the specific challenges it poses to AI models. ...

Seeing through Disaster Rubble in 3D with Ground-Penetrating Radar and Interactive Augmented Reality for Urban Search and Rescue
  • Citing Article
  • July 2022

Journal of Computing in Civil Engineering