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High-resolution power equipment recognition based on improved self-attention

Autor: Zhang, Siyi, Liu, Cheng, Li, Xiang, Zhai, Xin, Wei, Zhen, Li, Sizhe, Ma, Xun

The current trend of automating inspections at substations has sparked a surge in interest in the field of transformer image recognition. However, due to restrictions in the number of parameters in existing models, high-resolution images can't be dir

Externí odkaz: http://arxiv.org/abs/2311.03518

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Image Recognition of Oil Leakage Area Based on Logical Semantic Discrimination

Autor: Lin, Weiying, Liu, Che, Zhang, Xin, Wei, Zhen, Li, Sizhe, Ma, Xun

Implementing precise detection of oil leaks in peak load equipment through image analysis can significantly enhance inspection quality and ensure the system's safety and reliability. However, challenges such as varying shapes of oil-stained regions,

Externí odkaz: http://arxiv.org/abs/2311.02256

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Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation

Autor: Wang, Weixi, Zhong, Xichen, Li, Xin, Li, Sizhe, Ma, Xun

Overhead line inspection greatly benefits from defect recognition using visible light imagery. Addressing the limitations of existing feature extraction techniques and the heavy data dependency of deep learning approaches, this paper introduces a nov

Externí odkaz: http://arxiv.org/abs/2311.00979

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Transmission line condition prediction based on semi-supervised learning

Autor: Li, Sizhe, Ma, Xun, Liu, Nan, Jin, Yi

Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level. Aiming at the problem that existing models cannot take

Externí odkaz: http://arxiv.org/abs/2310.19756

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