Insulator Defect Detection Based on Attention Mechanism and Multi-scale Fusion
Aiming at addressing the suboptimal detection performance for insulator defect due to small scaleand suscepti-bility to background interference,the present work proposed a light-weight method of detecting insulator defects based on the YOLOv8n model.This method combines attention mechanism and multi-scale fusion technology to achieve improved accuracy and reduced model parameters.First an attention module was integrated into the feature extraction network.Sec-ond a weighted bi-directional feature pyramid network(BiFPN)with an added small target detection layer was employed.The proposed method was demonstrated by experiment to achieve a 1.9%increase in average detection accuracy while re-ducing model parameters,compared to the original approach,highlighting its effectiveness.