The state assessment of overhead transmission line fittings is crucial to the reliable operation of the line, and the detection of the fittings is an important part of the assessment work. In response to the heavy workload of manual labeling of datasets in identification and detection of fittings, as well as the difficulty of balancing high precision and rapidity, an improved transmission line fittings detection method based on YOLOX network is proposed. The fitting images captured by UAV are augmented with preprocessing to enrich the datasets. The backbone network adopts the enhancement methods of online Mosaic and Mixup. The transfer learning based on feature extraction is introduced and the cosine annealing learning rate is used for two-stage model training. The experimental results show that the mean average precision of the improved method for the detection of all types of fittings is improved by 18.32%. Compared with five mainstream detection models such as Faster R-CNN algorithm, the mean average precision of proposed method is the highest, and its detection speed is lower than YOLOv3’s, which can identify various types of fittings more quickly and accurately, and reduce the workload of manual labeling to a certain extent.