基于改进CenterNet的航拍绝缘子缺陷实时检测模型

doi:10.11896/jsjkx.210400142

Abstract

Abstract: Aiming at the problem that it is difficult to detect insulators and their defects in real time and efficiently in the course of electric patrol inspection of UAV,an improved insulator defect detection model based on CenterNet is proposed.Firstly,lightweight network EfficientNet-B0 is used to replace the original model’s feature extraction network ResNet18,which ensures the model extraction ability and speeds up its reasoning speed.Then,a feature enhancement module FEM is built,which distributes the weight of the feature channels after upsampling reasonably and suppresses invalid features.Using FPN (feature pyramid networks) for reference,the shallow and deep features are integrated to enrich the information of feature layer.Secondly,the coordination attention(CA) mechanism,which is a mixture of space and channel,is introduced into CenterNet-Head,which makes the prediction of category and location information more accurate.Finally,Soft-NMSis used to solve the problem of “single target and multiple frames” caused by inaccurate prediction of center points in the process of model detection.Experimental results show that the precision of the improved CenterNet is improved by 11.92%,the speed is increased by 8.95 FPS,and the model size is reduced by 54 MB.Compared with other detection models,the accuracy and speed are improved,which proves the real-time performance and robustness of the improved model.

Key words: Attention mechanism, CenterNet, Defect detection, Feature fusion, Insulator

CLC Number:

TM933

LI Fa-guang, YILIHAMU·Yaermaimaiti. Real-time Detection Model of Insulator Defect Based on Improved CenterNet[J].Computer Science, 2022, 49(5): 84-91.

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Real-time Detection Model of Insulator Defect Based on Improved CenterNet

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