Aiming at the problems of difficulty in detecting small targets in UAV inspection images, obstacles blocking targets and imbalance between positive and negative samples, a multi-target detection method based on improved Cascade R-CNN was proposed for transmission lines. The feature extraction network of the Cascade R-CNN was improved. Based on the basic network structure of ResNet101, a new 6-layer feature pyramid network structure was designed to achieve feature fusion, improving the detection ability of small targets and overlapping targets. The Gaussian Soft-NMS method was introduced to reduce the missed detection rate of the target with occlusion. The Focal loss was used to improve the loss function, alleviating the impact of imbalance between positive and negative samples on detection accuracy. During the training process, the data set was expanded based on data enhancement methods such as adding noise, brightness transformation and scaling, which improved the generalization performance of the training model. Experimental results show that the improved model can simultaneously detect three types of porcelain insulators, porcelain insulator defects, interphase rods, anti-vibration hammers and bird’ s nests under complex backgrounds. The average accuracy ( mAP) reaches 94. 1%, which provides a new idea for the intelligent inspection of transmission lines.