发表机构
Carleton University（卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TileNet，一种基于瓦片的CNN-SVM轻量级架构，用于无人机实时平屋顶缺陷检测，在双高度飞行图像上达到94.4%准确率，优于现有模型，实现安全高效检查。
AI 中文摘要
平屋顶是建筑围护结构中影响最大的组成部分之一，它同时决定结构性能和热效率，从而直接影响家庭能耗、碳排放和长期环境可持续性。及时检测屋顶缺陷对于减少供暖和制冷损失、防止霉菌生长等湿气引起的退化以及支持国家气候变化减缓目标至关重要。本文提出了一种基于无人机系统（UAS）的实时深度学习框架，利用双高度空中飞行期间捕获的实时图像自主检测缺陷。多分辨率飞行策略旨在帮助识别细小尺度缺陷和较大结构问题，从而实现更全面的评估。为了满足嵌入式无人机硬件严格的计算和功耗限制，所提出的框架集成了基于瓦片的架构和轻量级卷积神经网络-支持向量机（CNN-SVM）分类器，专为低延迟机载推理而设计。最终模型由五个卷积层和四个全连接层组成，最后一层为线性SVM头，在照片级划分（43,383张训练、3,869张验证和2,540张测试的瓦片化和增强图像）上实现了94.4%的平均测试准确率（95%置信区间±0.4%，基于三个随机种子），优于GoogLeNet（89.2%）和AlexNet（79.8%）。使用DJI Matrice 350 RTK无人机现场采集的真实无人机图像进行的实验评估表明，该系统支持快速、可重复和安全地检查屋顶，同时降低人员风险、降低运营成本，并实现更可持续的建筑维护。
英文摘要
Flat roofs are among the most influential components of the building envelope, governing both structural performance and thermal efficiency, and thereby contributing directly to household energy consumption, carbon emissions, and long-term environmental sustainability. Timely detection of roof defects is essential for reducing heating and cooling losses, preventing moisture-driven degradation such as mold growth, and supporting national climate-change mitigation goals. This paper presents a real-time, Unmanned Aerial System (UAS)-based deep learning framework that autonomously detects defects using live imagery captured during dual-altitude aerial passes. The multi-resolution flight strategy is designed to aid the identification of both small, fine-scale defects and larger structural issues, enabling more comprehensive assessments. To meet the strict computational and power constraints of embedded UAS hardware, the proposed framework integrates a tile-based architecture with a lightweight Convolution Neural Network-Support Vector Machine (CNN-SVM) classifier designed for low-latency onboard inference. The final model-comprising five convolutional layers and four dense layers, the last a linear SVM head, achieved a mean test accuracy of $94.4\%$ ($95\%$ confidence interval $\pm0.4\%$ over three seeds) on a photo-level split ($43,383$ training, $3,869$ validation, and $2,540$ test tiled and augmented images), outperforming GoogLeNet ($89.2\%$) and AlexNet ($79.8\%$). Experimental evaluations using real UAS imagery collected by onsite visits with DJI Matrice 350 RTK drone demonstrate that the system supports rapid, repeatable, and safe roof inspections while reducing human risk, lowering operational costs, and enabling more sustainable building maintenance.
CommentsJournal of Safety Science and Resilience
