Accurately detecting insulator defects is one of the main tasks of power grid maintenance. In response to the problems of low recognition accuracy of current insulator defect detection algorithms and large models that are difficult to deploy to mobile devices such as drones, a method based on YOLOv8 is proposed to improve the detection accuracy and lightweight the model. This method uses the feature fusion mode in a bi directional feature pyramid network BiFPN to fully fuse multi-scale features, and then integrates a deformable attention mechanism DAttention into the original algorithm to extract features with lower complexity. In addition, it introduces a fusion of average pooling and maximum pooling coordinate attention DAF-CA to enhance key information, and finally uses the minimum point distance based Intersection over Union MPDIoU as the loss function to improve the training effect of bounding box regression, thereby improving the accuracy of the algorithm. Multiple comparative experiments were conducted on the dataset, and the results showed that the proposed method achieved an average accuracy of about 91.0%. The model had a floating point count of 7.2 G and a parameter count of 2.07 M, respectively, and all performance indicators were superior to commonly used detection algorithms. This method can provide reference for intelligent inspection of power grids.