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.

中文翻译：

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FVCNet：电力线路巡检中基于特征视觉聚类网络的障碍物检测方法

电力线路巡检是消除电力线路隐患的重要手段。如何解决基于深度神经网络（DNN）的电力线路巡检由于多视角形状、小尺寸物体等问题而导致的精度低的问题是一个研究难题。本文建立了一种基于特征视觉聚类网络（FVCNet）的电力线路检测自动检测模型。首先，提出了一种用于电力线路检测的无监督聚类方法，并应用于构建能够识别多视图形状物体和增强物体特征的检测模型。然后，采用双线性插值方法进行特征增强方法，将增强后的高层语义和低层语义进行融合，解决目标尺寸小、样本单一的问题。本文将FVCNet应用于MS-COCO 2017数据集和自制电力线路巡检数据集，测试精度分别提升至61.2%和82.0%。与其他模型相比，特别是对于受多视图形状影响较大的类别，测试精度得到了显着提高。