Research purposes: Rail surface damage segmentation is an important part of railway engineering inspection. In order to address the challenge of timely detection of rail surface damage to prevent safety accidents and overcome the limitations of traditional detection methods, this paper proposes an automated damage segmentation algorithm that integrates multiple deep learning models, to provide a precise and efficient solution for the identification, analysis, and processing of rail surface damage.
Research conclusions: (1) A deep learning algorithm integrating target detection and semantic segmentation is proposed to achieve efficient recognition and accurate segmentation for rail surface damage features. (2) The practical results show that this algorithm has higher accuracy compared with several existing deep learning models, and the accuracy and average cross-merge ratio reach 99.56% and 83.89%, respectively. (3) This algorithm can accurately differentiate the blurred boundaries between rail damage and background, reduce data redundancy, and improve segmentation efficiency. It achieves high segmentation accuracy for multi-scale damage targets and the research conclusions can provide theoretical guidance for the automation of railway engineering inspection.
Key words
rail surface damage /
deep learning /
image segmentation /
railway engineering
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References
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