Insulator Defect Detection Based on Lightweight Network and Enhanced Multi-scale Feature Fusion
With the development of target detection algorithm embedded in UAV for insulator inspection of transmission towers,a YOLOv5-3S-4PH model based on lightweight network and enhanced multi-scale feature fusion is proposed to detect insulator defects in real time in view of the low detection speed,high network complexity and the difficulty of ac-curate detection of small defect targets.Firstly,the reconstructed ShuffleNetV2-Stem-SPP(3S)network is used as the backbone of YOLOv5,which reduces the amount of network parameters and calculation significantly.Secondly,the en-hanced multi-scale feature fusion network for small targets and four prediction heads(4PH)is added to enhance the network's perception of insulator defects.Combined with Mosaic-9 data enhancement and CIoU loss function,the loss of detection accuracy caused by lightweight is further compensated.Finally,the YOLOv5-3S-4PH model is applied to the self-made insulator dataset for verification.The experimental results show that mean average precision(mAP)is increased by 3%,the detection speed is increased by 81.8%,and parameters and calculation are decreased by 82.4%and 67%com-pared to original YOLOv5 model.Therefore,the proposed model is more suitable for real-time monitoring of insulator defects deployed on UAV platforms.
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