用自监督学习自动识别污水管道缺陷，模型更小、数据需求更低。
Self-Supervised Learning for Identifying Defects in Sewer Footage
- 采用自监督学习，无需大量标注数据即可训练
- 模型规模比现有方法小至少5倍，仅用10%数据达竞争性效果
- 适合资源有限地区推广，降低维护成本
污水管网是现代最昂贵的基础设施之一，需依赖专业人员进行耗时的人工巡检。本研究针对自动化检测需求，提出一种新颖的自监督学习（SSL）应用，为缺陷检测提供可扩展且低成本的解决方案。所提模型在性能上具有竞争力，且相比文献中其他方法，模型规模至少缩小5倍；在使用更大架构时，仅需10%的可用数据即可获得竞争性表现。研究结果表明，自监督学习有望在资源受限环境中彻底改变污水管网维护方式。
原文摘要 · Abstract (English)
Sewerage infrastructure is among the most expensive modern investments requiring time-intensive manual inspections by qualified personnel. Our study addresses the need for automated solutions without relying on large amounts of labeled data. We propose a novel application of Self-Supervised Learning (SSL) for sewer inspection that offers a scalable and cost-effective solution for defect detection. We achieve competitive results with a model that is at least 5 times smaller than other approaches found in the literature and obtain competitive performance with 10\% of the available data when training with a larger architecture. Our findings highlight the potential of SSL to revolutionize sewer maintenance in resource-limited settings.
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