PDP-CNN: A Deep Learning Model for Post-Hurricane Reconnaissance of Electricity Infrastructure on Resource-Constrained Embedded Systems at the Edge

Abstract

Unmanned aerial vehicles (UAVs) equipped with cameras have provided new capabilities for the reconnaissance of disaster-stricken areas. Deep learning-based computer vision algorithms enable the analysis of the captured images and the detection of damage to the built environment. If such analyses are conducted onboard in UAVs, they will provide real-time actionable information that is critical for the accelerated restoration of systems. However, conventional deep learning algorithms are computationally demanding. Moreover, the deployment of deep learning models with a large number of parameters in scenarios that require low-latency inference is prohibitive. To address this fundamental gap, we develop an efficient deep learning-based computer vision model of power distribution poles (PDPs) damage detection that is capable of onboard deployment in UAVs. Specifically, we propose a lightweight convolutional neural network (CNN) architecture called PDP-CNN that embodies multiscale feature operations and anchor-less object detection. This model is applied to a dedicated image database from a post-hurricane reconnaissance in PDPs. Results of extensive experiments show that PDP-CNN is capable of achieving high throughput, competitive accuracy, and efficient memory utilization on power-constrained embedded systems.