Abstract
Industrial smoke segmentation is critical for air-quality monitoring and environmental protection but is often hampered by the high cost and scarcity of pixel-level annotations in real-world settings. We introduce CEDANet, a human-in-the-loop, class-aware domain adaptation framework that uniquely integrates weak, citizen-provided video-level labels with adversarial feature alignment. Specifically, we refine pseudo-labels generated by a source-trained segmentation model using citizen votes, and employ class-specific domain discriminators to transfer rich source-domain representations to the industrial domain. Comprehensive experiments on SMOKE5K and custom IJmond datasets demonstrate that CEDANet achieves an F1-score of 0.414 and a smoke-class IoU of 0.261 with citizen feedback, vastly outperforming the baseline model, which scored 0.083 and 0.043 respectively. This represents a five-fold increase in F1-score and a six-fold increase in smoke-class IoU. Notably, CEDANet with citizen-constrained pseudo-labels achieves performance comparable to the same architecture trained on limited 100 fully annotated images with F1-score of 0.418 and IoU of 0.264, demonstrating its ability to reach small-sampled fully supervised-level accuracy without target-domain annotations. Our research validates the scalability and cost-efficiency of combining citizen science with weakly supervised domain adaptation, offering a practical solution for complex, data-scarce environmental monitoring applications.
Abstract (translated)
工业烟雾分割对于空气质量监测和环境保护至关重要，但由于实际场景中像素级标注的成本高昂且稀缺，这一过程往往受到限制。我们提出了CEDANet，这是一个人类参与的、基于类别感知领域的自适应框架，该框架独特地整合了由公民提供的弱视频级别标签与对抗性特征对齐技术。具体来说，我们通过使用公民投票来优化源训练分割模型生成的伪标签，并利用特定类别的领域判别器将丰富的源域表示转移到工业领域。 在SMOKE5K和定制的IJmond数据集上的全面实验表明，CEDANet在有公民反馈的情况下取得了0.414的F1分数和0.261的烟雾类别交并比（IoU），大大超过了基础模型的成绩，该模型分别得分0.083和0.043。这代表了F1分数五倍的增长以及烟雾类别IoU六倍的增长。 值得注意的是，在仅依靠公民约束伪标签的情况下，CEDANet能够达到与在有限的100张完全标注图像上训练的相同架构相似的表现水平，其F1得分为0.418，IoU为0.264。这表明CEDANet能够在不进行目标域注释的情况下，以小样本充分监督级别的精度完成任务。 我们的研究验证了结合公民科学与弱监督领域适应方法在规模和成本效益上的可行性，并提供了一种实用的解决方案来应对复杂且数据稀缺的环境监测应用挑战。
URL
https://arxiv.org/abs/2507.22002
