Towards intelligent railway monitoring: A novel hybrid deep learning architecture for railway obstacle detection
Fig 2
Visualization of the proposed architecture.
The two architectures, ResNet50 and Swin Transformer V2, each extract key features from the input images. The respective one-dimensional vectors are then fused. The fused vector is then processed by the Efficient Channel Attention (ECA) module [39], which highlights the most relevant features. This enhanced vector is passed through two fully connected layers to produce the final classification output.