Herein, a lightweight “You Only Look Once” (YOLOv5) ultrahigh voltage (UHV) transmission line insulator self-detonation detection algorithm based on the attention mechanism and Ghostnet is proposed to address the multi-scale features of UHV insulator aerial images. Small-scale defective pieces cannot be detected accurately by conventional algorithms in a complex power system inspection environment. To suppress the complex background interference, a YOLOv5-based spatial and channel convolution attention model is used to enhance the saliency of the target to be detected by weighting the feature layers and feature maps. The convolutional structure of the original cross stage partial (CSP_X) structure is replaced by the Ghostnet network to generate more similar feature maps by performing linear operations on the redundant feature maps so that more feature maps can be generated using fewer parameters to achieve a lightweight network and reduce the network parameters. To address the problem of missed and false detection caused by inadequate expression of the target features to be detected, the original neck feature pyramid network (FPN) + path aggregation network (PAN) structure is modified to a bidirectional FPN structure such that the target multi-scale features are effectively fused. Finally, SCYLLA-IoU is used as the loss function to accelerate the model convergence and improve the detection accuracy, and migration learning parameter sharing combined with the model training strategy of freeze and thaw training is performed for non-generalization of the network owing to small sample datasets. The effectiveness of the proposed algorithm is verified by training it with data obtained from UHV patrols. Experimental results show that the proposed algorithm accurately monitors insulator self-detonation targets in complex environments with an average detection accuracy of 97.4% and a significant reduction in the size of models and parameters, which has good practical value.