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Research on Unsupervised Low-Light Railway Fastener Image Enhancement Method Based on Contrastive Learning GAN.
Cai, Yijie; Liu, Xuehai; Li, Huoxing; Lu, Fei; Gu, Xinghua; Qin, Kang.
Afiliação
  • Cai Y; China Railway Wuhan Bureau Group Co., Ltd., Wuhan 430061, China.
  • Liu X; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Li H; Gemac Engineering Machinery Co., Ltd., Xiangyang 441000, China.
  • Lu F; Key Laboratory of Modern Manufacturing Quality Engineering in Hubei Province, Wuhan 430068, China.
  • Gu X; Gemac Engineering Machinery Co., Ltd., Xiangyang 441000, China.
  • Qin K; School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel) ; 24(12)2024 Jun 11.
Article em En | MEDLINE | ID: mdl-38931578
ABSTRACT
The railway fastener, as a crucial component of railway tracks, directly influences the safety and stability of a railway system. However, in practical operation, fasteners are often in low-light conditions, such as at nighttime or within tunnels, posing significant challenges to defect detection equipment and limiting its effectiveness in real-world scenarios. To address this issue, this study proposes an unsupervised low-light image enhancement algorithm, CES-GAN, which achieves the model's generalization and adaptability under different environmental conditions. The CES-GAN network architecture adopts a U-Net model with five layers of downsampling and upsampling structures as the generator, incorporating both global and local discriminators to help the generator to preserve image details and textures during the reconstruction process, thus enhancing the realism and intricacy of the enhanced images. The combination of the feature-consistency loss, contrastive learning loss, and illumination loss functions in the generator structure, along with the discriminator loss function in the discriminator structure, collectively promotes the clarity, realism, and illumination consistency of the images, thereby improving the quality and usability of low-light images. Through the CES-GAN algorithm, this study provides reliable visual support for railway construction sites and ensures the stable operation and accurate operation of fastener identification equipment in complex environments.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article