Herein, an improved insulator defect-detection algorithm, YOLOv5, is proposed to overcome the shortcomings, including inconspicuous target features and poor detection of small targets when detecting trapped insulators using unmanned aerial vehicles, which cannot satisfy both detection speed and accuracy. First, ConvNeXt is applied to the YOLOv5 reference network to improve its ability to extract the features of obscure targets. Moreover, a coordinate attention mechanism is introduced into the reference network to improve its detection accuracy with respect to small targets in an image. Then, the improved model is pruned to eliminate its redundant channels, thus reducing the number of model parameters and making the model more lightweight. The experimental results show that the improved model achieves an average detection accuracy of 93.84% with respect to the insulator-defect dataset IDID, which is 3.4 percentage points higher than the accuracy achieved by the original algorithm. Moreover, the highest detection rate achieved by the proposed algorithm is 166 frame/s, which is 69.4% higher than that achieved by the original algorithm. These results prove that the improved algorithm meets the requirements of real-time transmission-line detection.