用GAN增强老照片，让屋顶检测准确率提升40%以上
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery
- 先彩色化再超分辨，两阶段GAN增强老航拍图
- YOLOv11n在增强图上mAP超85%，比原图高40%
- 适合做城市历史变迁研究的学者和测绘人员
从历史航拍影像中精准识别屋顶对分析长期城市发展与人类聚居模式至关重要。然而，黑白模拟照片因空间分辨率低、缺乏色彩信息及档案退化，给现代目标检测框架带来挑战。为此，本研究提出基于生成对抗网络（GAN）的两阶段图像增强流程：首先使用DeOldify进行图像彩色化，再通过Real-ESRGAN实现超分辨率增强。增强后的图像用于训练和评估屋顶检测模型，包括Faster R-CNN、DETReg和YOLOv11n。结果表明，彩色化与超分辨率结合显著提升检测性能，其中YOLOv11n的平均精度（mAP）超过85%，相比原始黑白图像提升约40%，较仅彩色化提升20%。该方法有效弥合了档案影像与现代深度学习技术之间的差距，助力从历史航拍图中更可靠地提取建筑轮廓。代码与资源已公开于github.com/Pengyu-gis/Historical-Aerial-Photos。
原文摘要 · Abstract (English)
Precise detection of rooftops from historical aerial imagery is essential for analyzing long-term urban development and human settlement patterns. Nonetheless, black-and-white analog photographs present considerable challenges for modern object detection frameworks due to their limited spatial resolution, absence of color information, and archival degradation. To address these challenges, this research introduces a two-stage image enhancement pipeline based on Generative Adversarial Networks (GANs): image colorization utilizing DeOldify, followed by super-resolution enhancement with Real-ESRGAN. The enhanced images were subsequently employed to train and evaluate rooftop detection models, including Faster R-CNN, DETReg, and YOLOv11n. The results demonstrate that the combination of colorization with super-resolution significantly enhances detection performance, with YOLOv11n achieving a mean Average Precision (mAP) exceeding 85\%. This signifies an enhancement of approximately 40\% over the original black-and-white images and 20\% over images enhanced solely through colorization. The proposed method effectively bridges the gap between archival imagery and contemporary deep learning techniques, facilitating more reliable extraction of building footprints from historical aerial photographs. Code and resources for reproducing our results are publicly available at \href{https://github.com/Pengyu-gis/Historical-Aerial-Photos}{github.com/Pengyu-gis/Historical-Aerial-Photos}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。