While Vision Transformer-based approaches offer significant advantages in capturing global context and spatial relationships, they may have limitations when dealing with extreme scenarios like severe occlusion or variations in object scale and orientation. To improve the model's robustness in handling such challenges, several enhancements can be considered. One approach is to incorporate attention mechanisms that dynamically adjust the focus on different parts of the input, allowing the model to adapt to varying levels of occlusion. Additionally, integrating spatial priors or geometric constraints into the model architecture can help improve object localization accuracy in cases of extreme scale variations or orientation changes. Furthermore, exploring advanced data augmentation techniques specifically designed to simulate challenging scenarios can enhance the model's ability to generalize to diverse conditions. By continuously refining the network architecture and training strategies to address these limitations, the Vision Transformer-based approach can be further improved to handle more complex object detection scenarios effectively.