废旧-SAM-CLIP:组装基础模型用于典型的形状识别在废旧分类和评级.
Guangda Bao1, Wenzhi Xia1, Haichuan Wang1,2
1School of Metallurgical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了Scrap-SAM-CLIP (SSC),这是一个视觉语言模型,用于准确的3D废弃物维度推断. 通过改进废料形状识别和3D重建能力,SSC增强了工业视觉系统.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 工业自动化 工业自动化
背景情况:
- 2D方法难以从图像中推断绝对碎片尺寸.
- 准确的3D识别需要强大的废料形状识别和维度提取.
研究的目的:
- 开发一种视觉语言模型,用于精确的3D废弃物尺寸推断.
- 在工业废弃物识别中建立3D重建和维度提取的基础框架.
主要方法:
- 拟议的Scrap-SAM-CLIP (SSC),集成分段任何模型 (SAM) 和中文对比语言图像预训练 (CN-CLIP).
- 在自建的废弃数据集上微调单个SSC模块.
- 评估了针对细分的各种提示策略,使用组合的盒子和点提示显示最佳性能.
主要成果:
- 在分类中,SSC实现了95.3%的准确性,比Swin Transformer V2_base的性能优于2.9%.
- 微调SAM解码器在杂的提示下提高了稳定性,精度提高了高达15%.
- t-SNE可视化证实了SSC优越的功能学习能力.
结论:
- SSC的模块化组装策略使特定组件的优化成为可能,并提高了系统的解释性.
- 适应的基础模型被证明对工业视觉系统有效,完善了废弃物3D识别管道.
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