In response to the issues of sample imbalance, complex backgrounds, and insufficient detection accuracy of small targets in the images of insulators collected by unmanned aerial vehicles (UAVs) during power grid inspection, a method based on multiple improvement strategies of YOLOv5 is proposed. Firstly, large-resolution data is sliced to increase the proportion of small targets in the overall image. Secondly, the kmeans++ _CIoU method is employed to optimize anchor box selection, enabling the model to better adapt to insulator bounding boxes of different sizes within the dataset. Additionally, the CBAM attention mechanism is incorporated to enhance focus on insulator features from both channel and spatial perspectives. Finally, Focal Loss is incorporated to encourage the model to prioritize more challenging data, alleviating the impact of sample imbalance. The experimental results indicate that the detection method exhibits varying degrees of improvement in average precision as well as in the accuracy of detecting large, medium, and small targets.