CN-ViT- Visual Object Detection with VisionTransformer

Object detection has always been an important and challenging task in the field of computer vision. In recent years, Vision Transformers (ViT) have achieved remarkable results on image classification tasks, demonstrating its potential in vision tasks. In this paper, we propose CN-ViT, a novel Vision Transformer based visual object detection model.CN-ViT effectively improves the accuracy and robustness of object detection by combining the advantages of self-attention mechanism and convolutional neural network, and introducing the GCCA (Global Context Block and Coordinate Attention) module. In this paper, CN-ViT is evaluated on the Mini COCO standard dataset. The experimental results suggest that CN-ViT may outperform current mainstream object detection methods in terms of detection accuracy and speed. This study sheds light on the potential of Transformer architectures for complex visual tasks and offers valuable insights for future research in this area.