Insulator Positioning Detection and Infrared Fault Recognition Based on Improved YOLOv5s
In insulator positioning detection and thermal faults recognition,due to severe background interfer-ence in insulator infrared images,the average recognition accuracy is low.In order to achieve precise position and detect of insulator position and improve the reliability and accuracy of identifying its thermal faults,an im-proved insulator positioning detection and infrared faults recognition method based on YOLOv5s is proposed.Firstly,a new structure C3GC is proposed by integrating the global context attention mechanism with the C3 structure of YOLOv5s Backbone,which enhances the ability of the model to extract features and reduces its a-mount of calculation.Secondly,replacing the loss function with VariFocal Loss,the recall rate of the model is improved,which can reduce the problems of missed detections of model.Finally,by introducing transposed con-volution and dynamically learning the parameters that need to be supplemented,the loss of features of the mod-el during sampling process is reduced.The experimental and testing results show that compared with the origi-nal YOLOv5s,the improved method improves positioning accuracy by 1.3％,detection accuracy for fault points by 4％,average accuracy by 2.8％,and both accuracy and recall rate is improved.
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