Novel Deep Learning Model for Reconnaissance of Infrastructure on Drones
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.
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.