Accurate detection of insulator defects in transmission lines is crucial for ensuring the safety and stability of powertransmission.To address the challenges posed by complex and variable backgrounds

as well as the diverse forms of defects inaerial images for multi-defect detection of insulators

this paper proposes a method combining contrastive loss and parallelresidual attention for insulator multi-defect detection. Specifically

the parallel residual attention mechanism reduces themutual interference between channel and spatial attention

while introducing a residual structure to enhance the model's focuson defect areas

effectively addressing complex background issues while retaining key information.Contrastive loss maximizesthe differences between features of different defects and minimizes the differences between features of the same defect

improving the model's ability to discriminate and express feature consistency for multi-form defects. In addition

thecombination of focal shape Intersection over Union(IoU)and shape IoU optimizes both the shape and size of bounding boxes

as well as focus on high-quality bounding boxes

further enhancing bounding box regression accuracy.Experimental resultsshow that the proposed method outperforms current mainstream algorithms. Compared to the baseline model YOLOv8

theaccuracy of defect detection for insulator drop

damage

and flashover improved by 0.9%

6.0%

and 7.3%

respectively.Thisprovides reliable technical support for power emergency monitoring and analysis

thus more effectively ensuring the safety andstability of power transmission.