FVCNet: Detection obstacle method based on feature visual clustering network in power line inspection

Abstract

Power line inspection is an important means to eliminate hidden dangers of power lines. It is a difficult research problem how to solve the low accuracy of power line inspection based on deep neural network (DNN) due to the problems of multi-view-shape, small-size object. In this paper, an automatic detection model based on Feature visual clustering network (FVCNet) for power line inspection is established. First, an unsupervised clustering method for power line inspection is proposed, and applied to construct a detection model which can recognize multi-view-shape objects and enhanced object features. Then, the bilinear interpolation method is used to Feature enhancement method, and the enhanced high-level semantics and low-level semantics are fused to solve the problems of small object size and single sample. In this paper, FVCNet is applied to the MS-COCO 2017 data set and self-made power line inspection data set, and the test accuracy is increased to 61.2% and 82.0%, respectively. Compared with other models, especially for the categories that are greatly affected by multi-view-shape, the test accuracy has been improved significantly.