Fine Object Detection in Automated Solar Panel Layout Generation

Abstract

A solar panel layout is a diagram of a roof, with the roof edges and obstacles marked. Currently, the user has to manually draw boundary over each obstacle in a tedious and meticulous manner. In this work, we have built a framework using the existing object detection models. We have leveraged the power of traditional edge detection algorithms, fusing with the cutting-edge machine learning based object detection frameworks. This fusion results in a framework capable of detecting objects to their exact edges. Thus, the boundary of each obstacle in a solar panel can be generated automatically with the edge pixel count variation of less than 25% compared to the ground truth.