A transmission line is the lifeline of a power system, while bolts play the role of connecting fittings and tightening conductors. However, bolts at different positions have different definitions of defects, which belongs to the problem of visually indistinguishable. Aiming at visually indistinguishable bolt defects in transmission lines, this paper propose an end-to-end visually indistinguishable bolt defects detection method that is based on transmission line knowledge reasoning. Firstly, we use the End-to-end Object Detection with Transformers (DETR) as the basic model and augment it with the dilated encoder module to obtain the multi-scale features of the target. Then we design a transmission line image relative position encoding (TL-iRPE) to infer the bolt position knowledge. Finally, this paper designs a bolt attributes classifier and a bolt defects classifier. By combining the position knowledge and the attributes knowledge to assist bolt defect classifier in reasoning bolt defects, the accuracy of bolt defects detection is further improved. We have constructed the Visually Indistinguishable Bolt Defects Dataset (VIBD Dataset) and carried out experiments on the dataset. We call the bolt defects detection method combining position knowledge and attributes knowledge PA-DETR. Compared with other transmission line bolt defect detection methods, PA-DETR has more advantages in transmission line bolt defect detection.

Overhead transmission lines are important lifelines in power systems, and the research and application of their intelligent patrol technology is one of the key technologies for building smart grids. The main reason for the low detection performance of fittings is the wide range of some fittings’ scale and large geometric changes. In this paper, we propose a fittings detection method based on multi-scale geometric transformation and attention-masking mechanism. Firstly, we design a multi-view geometric transformation enhancement strategy, which models geometric transformation as a combination of multiple homomorphic images to obtain image features from multiple views. Then, we introduce an efficient multiscale feature fusion method to improve the detection performance of the model for targets with different scales. Finally, we introduce an attention-masking mechanism to reduce the computational burden of model-learning multiscale features, thereby further improving model performance. In this paper, experiments have been conducted on different datasets, and the experimental results show that the proposed method greatly improves the detection accuracy of transmission line fittings.