With the continuous advancement of deep learning technology, the application of Transformer architecture in computer vision tasks has achieved remarkable results. Through self-attention mechanism and multi-head attention mechanism, Transformer has demonstrated powerful global modeling capabilities in tasks such as image classification, object detection, and semantic segmentation, surpassing traditional convolutional neural networks (CNN). However, the computational complexity and memory consumption of the Transformer architecture are still its bottleneck in large-scale image processing. This paper systematically explores the efficient implementation and optimization methods of the Transformer architecture in computer vision, focusing on the application and effects of optimization schemes such as sparse attention, low-rank approximation, and hybrid architecture. Experimental results show that the optimized Transformer has achieved significant performance improvements on multiple standard datasets (ImageNet, COCO, and Cityscapes). The Top-1 accuracy of ViT on ImageNet reached 84.5%, while the mAP of DETR on COCO increased to 43.0%. Despite the challenges, with the development of optimization technology, the application prospects of Transformer in the field of computer vision are broad, and in the future, various optimization strategies and hardware acceleration technologies will be used to further improve its computational efficiency and application performance.