Swin-RGC: Swin-Transformer With Recursive Gated Convolution for Substation Equipment Non-Rigid Defect Detection

Autor: Hui Li, Jie Zhang, Rui Li, Hui Zhang, Le Zou, Shujuan Liu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: IEEE Access, Vol 11, Pp 72655-72664 (2023)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2023.3289874
Popis: Substation equipment defects are important factors affecting the safe operation of power grids. However, many non-rigid defects have low detection accuracy and poor robustness,due to boundary ambiguity, irregular shape and tiny size. To address these problems,we propose a swin-transformer with recursive gated convolution framework for substation equipment non-rigid defect. Firstly, in order to effectively detect non-rigid defect objects to improve the discriminability of image features, we design the Swin-Transformer with Recursive Gated Convolution(Swin-RGC) framework to extract the interaction features between spaces in the deep model. Secondly, to avoid the loss of object location information, the Task-aligned One-stage Object Detection(TOOD) head is improved by fusing Coordinate Attention modules. Finally, a substation equipment defect detection dataset is established to provide a baseline for detecting non-rigid defects in substation power equipment. Experiment results on our dataset demonstrate that our proposed method achieves the performance of 69.9% Mean Average Precision (mAP) in the substation equipment non-rigid defect detection, which outweighs the state-of-the-art approaches.
Databáze: Directory of Open Access Journals