Abstract: With the rapid advancement of general artificial intelligence technology, the application of foundational models in various fields has gained increasing attention. In the domain of image segmentation, the "Segment Anything Model" (SAM), as a core foundational model, has demonstrated significant advantages in improving both image understanding and processing efficiency. While SAM has shown strong performance in image segmentation tasks, there remains considerable room for optimization in areas such as power consumption, computational efficiency, and adaptability to diverse application scenarios. This paper provides an in-depth exploration of potential improvements to SAM across several key dimensions, including enhancing speed and computational efficiency, improving model accuracy and robustness, increasing adaptability and generalization, optimizing prompt engineering, and boosting data utilization and transfer learning capabilities. These enhancements aim to enable SAM to not only sustain high efficiency in more complex tasks but also better meet the requirements of various fields and application contexts. Additionally, this paper summarizes the practical applications of SAM in various fields, including medical imaging, remote sensing, and mechanical industries, demonstrating its suitability and challenges in different scenarios. Moreover, this paper provides a detailed overview of commonly used datasets and evaluation metrics in the field of image segmentation. Through experimental comparative analyses, the impact of Vision Transformer variants on SAM’s performance is assessed, alongside performance evaluations of enhanced models such as Efficient SAM, EfficientViT-SAM, MobileSAM, and Robust SAM. The challenges faced by SAM and its improved models in real-world applications are also discussed, and future research directions are proposed. The aim is to provide researchers with a comprehensive understanding of the advancements and applications of SAM and its variants, offering insights that may inform the development of new models.

摘要： 随着通用人工智能技术的快速发展，基础模型在多个领域的应用日益受到广泛关注。在图像分割领域，“分割一切模型”（Segment Anything Model, SAM）作为一种核心基础模型，在提升图像理解和处理效率方面展现出了显著优势。尽管SAM在图像分割任务中表现出色，但在功耗、计算效率以及在不同应用场景中的适应性等方面，仍然存在一定的优化空间。为此，文中从多个维度对SAM的改进方向进行了深入探索，包括提升速度和计算效率、增强模型的精度与鲁棒性、提高模型的适应性与通用性、优化提示工程设计，以及提升数据利用效率和强化迁移学习能力等方面。通过这些改进，SAM不仅能够在更复杂的任务中保持高效性能，还能更好地适应各领域和应用场景的需求。在此基础上，总结SAM在医学、遥感、机械等多个领域中的实际应用，展示了其在不同场景下的适用性与挑战。此外，文中还详细介绍了图像分割领域常用的数据集和评价指标，通过实验对比分析，进一步评估了Vision Transformer变体对SAM性能的影响，以及Efficient SAM、EfficientViT-SAM、MobileSAM和Robust SAM等改进模型的性能表现。最后，总结了SAM及其改进模型在实际应用中面临的挑战，并展望了未来的发展方向，旨在帮助科研工作者更全面地了解SAM及其变体的改进与应用，为新模型的提出提供启发。