分割
计算机科学
人工智能
计算机视觉
曲面(拓扑)
对象(语法)
GSM演进的增强数据速率
瓦片
图像分割
材料科学
几何学
数学
复合材料
作者
Kechen Song,Wenqi Cui,Yu Han,X Li,Yunhui Yan
标识
DOI:10.32604/cmc.2024.048451
摘要
Segment Anything Model (SAM) is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation, it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation, especially for complex industrial scenes with limited training data.However, its segmentation ability for specific industrial scenes remains unknown.Therefore, in this work, we select three representative and complex industrial surface defect detection scenarios, namely strip steel surface defects, tile surface defects, and rail surface defects, to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation, it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at: https:// github.
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