Geometric characteristic learning R-CNN for shockproof hammer defect detection

Abstract

Shockproof hammers are key components of power transmission lines. Aiming at the problems regarding the same kind of defect having various manifestations, different defects having similarities, and defect data being difficult to obtain, a geometric characteristic learning region-based convolutional neural network (GCL R-CNN) is proposed to detect shockproof hammer defects in aerial images of transmission lines. First, a GCL module is proposed for the first time and introduced in the Faster R-CNN, and artificial samples are generated via 3D modeling. Second, to make the model pay more attention to the geometric characteristics of the observed shockproof hammer, artificial samples with monochromatic backgrounds are used to guide the neural network training process. In this way, the model can better learn the salient features of the shockproof hammer defects and the model’s ability to distinguish normal samples from defective samples is enhanced. Finally, in the case of few samples of shockproof hammer defects, artificial samples with real backgrounds are used to expand the training set and improve the accuracy of shockproof hammer defect detection. Experimental results on real aerial images of transmission lines show that the proposed model can accurately detect normal shockproof hammers, missing shockproof hammer heads and tilted shockproof hammers, with detection accuracies of 93.8%, 89.94% and 66.22%, respectively. The above results show that the proposed model can realize the detection of two types of defects in the shockproof hammer, that is, the fault diagnosis of the shockproof hammer.