Nacala-Roof-Material：用于屋顶检测、分类与分割以支持蚊媒疾病风险评估的无人机影像
Nacala-Roof-Material: Drone Imagery for Roof Detection, Classification, and Segmentation to Support Mosquito-borne Disease Risk Assessment
AI总结：
本文发布莫桑比克无人机屋顶影像数据集 Nacala-Roof-Material，用于疟疾风险相关的屋顶检测、分类与分割，并提出改进的深度有序分水岭变体以提升多任务分割性能。
摘要：
由于低质量住房，特别是某些屋顶特征与疟疾风险增加相关，基于遥感影像的屋顶类型分类可以支持疟疾风险评估，从而有助于预防该疾病。为了支持这一领域的研究，我们发布了 Nacala-Roof-Material 数据集，该数据集包含来自莫桑比克的高分辨率无人机图像，并带有标注房屋轮廓和指定屋顶类型的相应标签。该数据集定义了一个多任务计算机视觉问题，包括目标检测、分类和分割。此外，我们在该数据集上对多种最先进的方法进行了基准测试。以经典 U-Net、YOLOv8 以及基于预训练 DINOv2 的自定义解码器作为基线。我们表明，每种方法各有优势，但没有一种方法在所有任务上都表现最优，这凸显了我们的数据集在未来多任务学习研究中的潜力。虽然这些任务密切相关，但目标的精确分割并不一定意味着精确的实例分离，反之亦然。我们通过引入深度有序分水岭（DOW）方法的一种变体来解决这一普遍问题，该变体额外分离目标内部，从而改进目标轮廓描绘与分离。我们表明，我们的 DOW 变体是一种通用方法，能够同时提升 U-Net 和 DINOv2 骨干网络的性能，从而在语义分割和实例分割之间实现更好的权衡。
英文摘要：
As low-quality housing and in particular certain roof characteristics are associated with an increased risk of malaria, classification of roof types based on remote sensing imagery can support the assessment of malaria risk and thereby help prevent the disease. To support research in this area, we release the Nacala-Roof-Material dataset, which contains high-resolution drone images from Mozambique with corresponding labels delineating houses and specifying their roof types. The dataset defines a multi-task computer vision problem, comprising object detection, classification, and segmentation. In addition, we benchmarked various state-of-the-art approaches on the dataset. Canonical U-Nets, YOLOv8, and a custom decoder on pretrained DINOv2 served as baselines. We show that each of the methods has its advantages but none is superior on all tasks, which highlights the potential of our dataset for future research in multi-task learning. While the tasks are closely related, accurate segmentation of objects does not necessarily imply accurate instance separation, and vice versa. We address this general issue by introducing a variant of the deep ordinal watershed (DOW) approach that additionally separates the interior of objects, allowing for improved object delineation and separation. We show that our DOW variant is a generic approach that improves the performance of both U-Net and DINOv2 backbones, leading to a better trade-off between semantic segmentation and instance segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
