An intelligent monitoring method of underground unmanned electric locomotive loading process based on deep learning method

Abstract

AbstractThe intelligent monitoring of electric locomotive loading is crucial in unmanned underground systems. A CNN-based monitoring scheme with migration learning was proposed to address efficiency, abnormality, and data acquisition challenges. Locomotive loading datasets are transformed, augmented, and equalized. Our model improves performance and training by modifying the fully connected layer, using optimized learning rate decay and adaptive algorithms. Training in PyTorch, the optimized VGG19-EL migration network achieves 99.85% recognition for 2-classifications, while the optimized RESNET50-EL migration network achieves 97.3% for 10-classifications. Overall, this study proposes a reliable and efficient model for liberating workers and monitoring locomotive loading.

Keywords

• Underground mine
• unmanned driving
• electric locomotive
• intelligent monitoring
• deep learning
• image recognition