Comer-Line-Prediction based Water-tank Detection and Localization

Abstract

Water tanks on the roof of buildings require regular labor-costing inspection, and object detection can be used to automate the task. Current detection frameworks have several drawbacks when they are applied: (1) The output horizontal rectangular boxes cannot provide arbitrary quadrilateral detection representations; (2) False positive results may easily appear when key-point based models are used. In this paper, we propose a novel detection framework: Corner-Line-Prediction, which generates tight quadrilateral detection results of the tank blocks. Our model is built on key point detection network to detect corner points precisely. And an original line predictor is integrated to recognize unique tank edges, such that numerous false positive detections can be suppressed. Experimental results show that our Corner-Line-Prediction (CLP) framework outperforms state- of-the-art detection algorithms in average-precision (AP) and produces better localization results, compared with mainstream general detection models.