基于改进的R-FCN航拍巡线图像中的绝缘子检测方法

doi:10.11896/j.issn.1002-137X.2019.03.024

Abstract

Abstract: In the case of partial occlusion of insulator target in aerial inspection images,the region-based fully convolutional networks (R-FCN) model is used for detection,however,the insulator target detection effect is poor and the detection frame cannot completely fit the target.Based on this,this paper proposed an insulator target detection method based on modified R-FCN in aerial inspection image.Firstly,according to the aspect ratio feature of insulator targets,the aspect ratios of proposals in the R-FCN model are modified to 1∶4,1∶2,1∶1,2∶1,4∶1.Then,in view of the occlusion problem in insulator image,an adversarial spatial dropout network (ASDN) layer is introduced into the R-FCN model to generate the samples of incomplete target feature by masking part of feature map,which can improve the detection performance of the model for samples with poor target feature.The average detection rate of R-FCN model reaches 77.27% on the dataset containing 7433 insulator targets.The average detection rate of the modified R-FCN detection method is 84.29%,which improves 7.02%,and the detection frame is more suitable for the target.

Key words: Database, Mask, Recommended box ratio, R-FCN

CLC Number:

TN919.8

ZHAO Zhen-bing, CUI Ya-ping, QI Yin-cheng, DU Li-qun, ZHANG Ke, ZHAI Yong-jie. Detection Method of Insulator in Aerial Inspection Image Based on Modified R-FCN[J].Computer Science, 2019, 46(3): 159-163.

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Detection Method of Insulator in Aerial Inspection Image Based on Modified R-FCN

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