针对监控管道检测中多标签噪声问题，提出更优的样本选择方法。
When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections
- 基于CoSELFIE改进的混合样本选择策略，应对复杂标签噪声。
- 在真实与合成噪声下均优于传统小损失筛选方法。
- 适合从事工业视觉质检、智能巡检系统研发者参考。
城市排水管网维护依赖高效的闭路电视（CCTV）巡检，其海量历史数据常含标签噪声。尽管单标签分类中已有诸多抗噪方法，但多标签分类中的噪声问题仍被忽视。本文将Co-teaching、CoSELFIE和DISC三种单标签抗噪样本选择方法适配至多标签场景，发现仅依赖小损失筛选效果有限。为此，提出新型多标签混合样本选择方法MHSS，基于CoSELFIE构建。实验表明，该方法在合成复杂噪声与真实噪声场景下均表现更优，推动了CCTV管道巡检自动化的发展。
原文摘要 · Abstract (English)
The maintenance of sewerage networks, with their millions of kilometers of pipe, heavily relies on efficient Closed-Circuit Television (CCTV) inspections. Many promising approaches based on multi-label image classification have leveraged databases of historical inspection reports to automate these inspections. However, the significant presence of label noise in these databases, although known, has not been addressed. While extensive research has explored the issue of label noise in singlelabel classification (SLC), little attention has been paid to label noise in multi-label classification (MLC). To address this, we first adapted three sample selection SLC methods (Co-teaching, CoSELFIE, and DISC) that have proven robust to label noise. Our findings revealed that sample selection based solely on the small-loss trick can handle complex label noise, but it is sub-optimal. Adapting hybrid sample selection methods to noisy MLC appeared to be a more promising approach. In light of this, we developed a novel method named MHSS (Multi-label Hybrid Sample Selection) based on CoSELFIE. Through an in-depth comparative study, we demonstrated the superior performance of our approach in dealing with both synthetic complex noise and real noise, thus contributing to the ongoing efforts towards effective automation of CCTV sewer pipe inspections.
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