Pedestrain detection for low-light vision proposal
This addresses pedestrian detection for low-light vision applications, but it appears incremental as it combines existing techniques without demonstrated novelty.
The paper tackles pedestrian detection in low-light conditions by fusing infrared and visible images, proposing to use a Vision Transformer model on the fused data and comparing it to YOLOv5, but no concrete results or numbers are provided.
The demand for pedestrian detection has created a challenging problem for various visual tasks such as image fusion. As infrared images can capture thermal radiation information, image fusion between infrared and visible images could significantly improve target detection under environmental limitations. In our project, we would approach by preprocessing our dataset with image fusion technique, then using Vision Transformer model to detect pedestrians from the fused images. During the evaluation procedure, a comparison would be made between YOLOv5 and the revised ViT model performance on our fused images