Colorectal cancer ranks among the most common and deadly cancers, emphasizing the need for effective early detection and treatment. To address the limitations of traditional colonoscopy, including high miss rates due to polyp variability, we introduce the Pyramid Vision Transformer Adapter Residual Network (PVTAdpNet). This model integrates a U-Net-style encoder-decoder structure with a Pyramid Vision Transformer backbone, novel residual blocks, and adapter-based skip connections. The design enhances feature extraction, dense prediction, and gradient flow, supported by squeeze-and-excitation attention for improved channel-wise feature refinement. PVTAdpNet achieves real-time, accurate polyp segmentation, demonstrating superior performance on benchmark datasets with high mDice and mIoU scores, making it highly suitable for clinical applications. PVTAdpNet obtains a high Dice coefficient of 0.8851 and a mean Intersection over Union (mIoU) of 0.8167 on out-of-distribution polyp datasets. Evaluation of the PolypGen dataset demonstrates PVTAdpNet's capability for real-time, accurate performance within familiar distributions. The source code of our network is available at https://github.com/ayousefinejad/PVTAdpNet.git