Power-DETR: end-to-end power line defect components detection based on contrastive denoising and hybrid label assignment

Document Type

Article

Publication Date

10-1-2024

Abstract

Maintenance of power transmission lines is essential for the safe and reliable operation of the power grid. The use of deep learning-based networks to improve the performance of power line defect detection faces significant challenges, such as small target sizes, shape similarities, and occlusion issues. In response to these challenges, a transformer-based end-to-end power line detection network called Power-DETR is introduced. Initially, building upon Deformable DETR, a large pre-trained model (Swin-large) is utilized to increase the number of multi-scale features, and activation checkpoint technology is applied to ensure effective training within limited memory capacity. Subsequently, a contrastive denoising training strategy is integrated to combat ambiguity and instability of the Hungarian matching algorithm during training, aiming to expedite model convergence. Additionally, a hybrid label assignment strategy combining OHEM and cost-based ATSS is proposed to provide the model with high-quality queries, ensuring adequate training for the decoder and enhancing encoder supervision. Experimental results substantiate the efficacy of the proposed Power-DETR model as a novel end-to-end detection paradigm, surpassing both one-stage and two-stage detection models. Furthermore, the model demonstrates a significant 15.7% enhancement in mAP0.5 compared to the baseline.

Identifier

85205064317 (Scopus)

Publication Title

IET Generation, Transmission and Distribution

External Full Text Location

https://doi.org/10.1049/gtd2.13275

e-ISSN

17518695

ISSN

17518687

First Page

3264

Last Page

3277

Issue

20

Volume

18

Grant

61871182

Fund Ref

National Natural Science Foundation of China

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