基于Segment Anything Model的高效刀具磨损分割
Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model
AI总结：
提出一种基于Segment Anything Model并集成U-Net作为自动化提示生成器的高效刀具磨损分割方法，在有限训练数据下仍能实现准确分割，优于U-Net。
摘要：
刀具磨损状况影响工件的表面质量及其最终几何精度。本研究提出了一种基于Segment Anything Model的高效刀具磨损分割方法，该方法集成了U-Net作为自动化提示生成器，以简化刀具磨损检测过程。我们的评估涵盖了三种Point-of-Interest生成方法，并进一步研究了训练数据集大小的变化和U-Net训练强度对最终磨损分割结果的影响。结果一致突显了本方法优于U-Net的优势，强调其即使在训练数据集有限的情况下也能实现准确的磨损分割。这一特性凸显了其在数据集可能受限的工业场景中的潜在适用性。
英文摘要：
Tool wear conditions impact the surface quality of the workpiece and its final geometric precision. In this research, we propose an efficient tool wear segmentation approach based on Segment Anything Model, which integrates U-Net as an automated prompt generator to streamline the processes of tool wear detection. Our evaluation covered three Point-of-Interest generation methods and further investigated the effects of variations in training dataset sizes and U-Net training intensities on resultant wear segmentation outcomes. The results consistently highlight our approach's advantage over U-Net, emphasizing its ability to achieve accurate wear segmentation even with limited training datasets. This feature underscores its potential applicability in industrial scenarios where datasets may be limited.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
