ODOT Project



This project is intended to comprehensively develop, test and deliver 6 UAVs with sensors, loggers, communications, and related equipment, operating protocols, and related robot control software packages.

  • It to be readily deployed statewide by an ODOT team to facilitate data collection with its payload of sensors
  • The project aims at the following:
    • Processing of data collected by the UAVs for actionable information by the user for various purposes at Ohio Department of Transportation.
    • Based on data interpretation and UAVs platforms, coordinate with various other teams including but not limited to first responders viz. police, fire, medical, etc.
    • Providing an administrative platform (e.g., via user-friendly but secure website) in a timely manner to control the UAVs in action.




Processing of video information to remove smoke

A major issue in the use of cameras mounted on UAVs for fire detection is the presence of smoke that occludes the hot spots in videos. This project focused on reconstructing images from video of scenes occluded by thick smoke and developed a method for filtering out the smoke occlusions in fire image streams using Proper Orthogonal Decomposition (POD). The method works by extracting frames from the video. It is assumed that the images are taken from camera with static background and moving smoke. Using POD, the modes corresponding to the dynamic part of the image sequence representing the smoke are identified and then filtered out to obtain a clear background. The smoke removal technique thus restores the original background that contains the fire information. The technique is applied to a number of sample videos and it is demonstrated that the smoke is sufficiently removed from the video with the background information intact. Following figure shows a sample of original and filtered image obtained from a laboratory experiment.




Estimation of Spatio-Temporal Processes via Online Filtering

Wildfire growth is typically modeled using a Partial Differential Equation (PDE) that can be solved using grid-based finite difference or finite element methods using some boundary and initial conditions. Given the geographical spread of the environment over which such events occur and with the temporal aspect factored in, data from sensing systems for such processes are very high dimensional in nature, and thus developing a filtering mechanism that incorporates sensing data for online and real-time estimation and prediction for applications can be very challenging due to the computational complexity involved. In this project, we developed a Kalman filtering based method harnessing concepts in Proper Orthogonal Decomposition (POD) for dimensional reduction. The rationale behind the proposed approach is that methods such as the POD facilitate reduced-order modeling, thus reducing the computational complexity. Implemented in a grid-based model of the environment, the approach can eventually be integrated into Geographic Information Systems (GIS) for effective utilization of information and decision making for incident managers. Following figure shows some results and demonstrates the effectiveness in removing noise in measurements.