Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

Abstract

Automatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.

Electrical insulator defect detection with incomplete annotations and imbalanced samples

“…Based on the aforementioned analysis, we further compared the performance of YOLO v5, Faster RCNN, and Pi-Index on unannotated insulators. recognized the unannotated insulators at positions (7) and (10). Therefore, both annotated and unannotated experimental results proved the effectiveness of Pi-Index.…”

“…For the small-scale insulators, all methods correctly identify and accurately locate those at positions (1)-( 3) and (7). The detection errors of each method are mainly concentrated on positions (5) and (13).…”

“…In the first column of row (a), there is one insulator string and 13 insulators. In detail, the insulators at positions (1)-( 4) and ( 9)-( 12) are labelled as "Good", while those at positions ( 5), (7), and ( 13) are labelled as "Broken". However, the insulators at positions ( 6) and ( 8) are not labelled.…”

“…For the relatively small insulators, all methods correctly classified the insulators at positions (1), (3), and (10). The positions that are easily undetected or misclassified by these methods are ( 6), (7), and (11). According to the IDID's annotations, M-YOLO v3 missed the insulators at positions ( 4), (6), and (7).…”

“…where 𝛼 d and 𝜆 represent the weighing factor and focusing parameter, respectively. The other notations refer to Equation (7). In practice, 𝛼 d and 𝜆 are separately set to 0.25 and 2, according to the parameter setting in [48].…”