Abstract: In order to realize the real-time and fast inspection of insulator defects in power transmission lines, a fast defect detection arithmetic FasterNet-YOLOv5 is proposed by combining FasterNet-tiny and YOLOv5-s-v6.1 network model improvement. Firstly, a FasterNet network with a small number of parameters and faster reasoning speed is introduced to replace the original CSPDarkNet53 backbone network to speed up the detection speed of the network. Then, the DFC-FasterNet module is designed in the backbone feature extraction network by combining the decoupled fully connected (DFC) mechanism proposed by the GhostNetv2 network, and the DFC attention mechanism in the module can increase the receptive field during the feature extraction process to improve the detection accuracy of the network. Finally, for the case of glass insulator self-blast defects with smaller targets and more complex background, the Neck module is redesigned, and the BiFPN-F feature fusion module is proposed to enable the network to more accurately localize the insulator defect region. The experimental results show that the improved algorithm can locate quickly and accurately, its mean average precision (mAP) reaches 93.3%, which is improved by 5.67% compared with the pre-improvement, and the detection speed reaches 45.7 Hz, which is nearly one times higher than the pre-improvement. Meanwhile, compared with the latest YOLOv8n and YOLOv7-tiny, the improved FasterNet-YOLOv5 has more advantages in detecting the self-destructive defects in terms of accuracy and speed, and the proposed algorithm can locate and identify insulators and their self-destructive defects in real time more quickly.