English(EN) Vision Transformer-Based Multi-Level Feature Fusion for Multi-Label Sewer Defect Classification

新型Vision Transformer模型提升下水道缺陷分类能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了Sewer-Transformer-ML，一种新颖的Vision Transformer模型，用于下水道缺陷的多标签分类。该模型集成了多层次特征融合，并在Sewer-ML测试集上取得了最先进的性能，在$F2_{ ext{CIW}}$指标上显著优于第二名的方法。此外，还为资源受限环境引入了两种轻量级架构Sewer-MobileNet-ML和Sewer-Mobile-TransNet，在大幅减少参数量的同时保持了高精度。 AI

影响该研究为自动化下水道检测和民用基础设施的轻量级模型设计提供了计算基础。

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arXiv cs.CV TIER_1 English(EN) · Xu Fang, Zhuoran Wang, Qing Li, Shengyu Zhang, Guanzhi Deng, Jianbiao He, Qingquan Li · 2026-09-11 04:00

基于Vision Transformer的多层次特征融合用于多标签下水道缺陷分类

arXiv:2609.11375v1 Announce Type: new Abstract: Automated classification of sewer defects is essential for infrastructure condition assessment and maintenance decision-making, but existing deep learning methods struggle to balance classification accuracy and computational complex…

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基于Vision Transformer的多层次特征融合用于多标签下水道缺陷分类

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