A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization

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Abstract
Railroad construction sites are high-risk environments where monitoring personnel safety is critical for preventing accidents and enhancing construction efficiency. Traditional manual monitoring and image processing methods exhibit deficiencies in real-time performance and accuracy. This paper proposes a railway worker detection method based on improved support vector machines (ISVM), while using non-local mean noise reduction and histogram equalisation pre-processing techniques to optimise image quality to improve detection efficiency and accuracy. Multiscale features are then extracted with Inception v3 and combined with principal component analysis (PCA) for dimensionality reduction. Finally, an SVM classification algorithm is employed for personnel detection. To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. This method provides technical support for railroad construction safety monitoring and effectively addresses personnel detection tasks in complex construction environments.
Funding Information
  • Guangxi Science and Technology Base and Talent Project for Guangxi Science and Technology Plan Project: Construction of Guangxi Transportation New Technology Transfer Center Platform (Guike AD23026029, 20223230)
  • Construction of Scientific and Technological Innovation Bases for Science Research and Technology Development Plan Project of Nanning City: Intelligent Operation and Maintenance of Transportation Infrastructure and Health Monitoring Platform (Guike AD23026029, 20223230)

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