Novel Deep Learning Model for Reconnaissance of Infrastructure on Drones

The Need

In disaster-stricken areas, timely and accurate reconnaissance is paramount for effective response and recovery efforts. Traditional methods of assessing damage to critical infrastructure, such as power distribution poles, often involve time-consuming manual inspections, leading to delays in restoration. There is an urgent need for a tool that can provide real-time actionable information to accelerate the assessment and restoration of essential systems in the aftermath of disasters.

The Technology

This critical need is met by a cutting-edge technology: Unmanned Aerial Vehicles (UAVs) equipped with an efficient deep learning-based computer vision model. This lightweight convolutional neural network architecture is specifically designed for onboard deployment in UAVs. The novel architecture utilizes multiscale feature operations and anchor-less object detection to analyze images captured by UAV cameras.

Commercial Applications

  • Disaster Response and Recovery: UAVs can rapidly assess damage to power distribution poles in disaster-stricken areas, providing critical information for prioritizing restoration efforts.
  • Infrastructure Inspection: Beyond disaster scenarios, our technology can be applied to routine infrastructure inspections, reducing the need for costly and time-consuming manual assessments.
  • Environmental Monitoring: UAVs can be employed for environmental monitoring, detecting damage to power infrastructure caused by natural elements like storms and earthquakes.


  • Real-time Actionable Information: The model provides instant, on-the-fly analysis of captured images, enabling immediate decision-making for disaster response teams.
  • Efficient Resource Utilization: Our lightweight computational architecture ensures efficient memory utilization and high throughput, even on power-constrained embedded systems.
  • Cost Savings: By automating the assessment of damage to critical infrastructure, organizations can significantly reduce labor costs associated with manual inspections.

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