基于CNN的砌体裂缝检测中真实与合成数据的平衡
Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection
AI总结：
针对砌体裂缝检测中真实数据不足的问题，提出用合成数据补充训练，通过调整真实与合成数据比例，发现20%真实数据加合成数据即可达到甚至超越纯真实数据的效果。
摘要：
裂缝是建筑健康的关键指标，早期识别对于防止有害损害至关重要。深度学习（DL）的进展，特别是卷积神经网络（CNN），已实现可扩展的自动裂缝检测解决方案。然而，CNN性能高度依赖于大规模多样化数据集的可用性，这对于砌体等复杂表面尤其具有挑战性。收集足够的真实数据耗时，而公开数据集可能不充分。为解决这一限制，我们探索生成合成裂缝数据，以补充真实数据并提高训练效果。真实数据集由从博洛尼亚及周边地区建筑收集的砌体裂缝图像组成。相比之下，合成数据集使用裂缝叠加工具生成，该工具以受控方向和位置向背景图像添加裂缝。使用真实数据集训练多种DL架构，以确定最佳性能模型（InceptionV4），用于生成数据的实验。通过改变真实与合成数据的比例，在InceptionV4上测试了六种训练场景，并在由真实图像组成的测试集上使用F1分数和平均交并比（mIoU）指标进行评估。结果表明，在合成数据上训练加上少量20%真实数据，可获得与仅使用真实数据训练相当的结果。此外，20/80（合成/真实）场景实现了76%的F1分数和80%的平均IoU，优于纯真实情况。可以看出，该方法展示了合成数据在减少收集工作同时提高裂缝检测准确性的潜力。
英文摘要：
Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages. Advances in deep learning (DL), particularly convolutional neural networks (CNNs), have enabled scalable solutions for automated crack detection. However, CNN performance strongly depends on the availability of large and diverse datasets, which is particularly challenging for complex surfaces such as masonry. Collecting sufficient real data is time-consuming, while publicly available datasets may not be adequate. To address this limitation, we explored generating synthetic crack data, which complements real data and improves training effectiveness. The real dataset consists of masonry crack images collected from buildings in Bologna and surrounding areas. In contrast, the synthetic dataset was generated using a crack overlay tool that adds cracks to background images in a controlled orientation and placement. The real dataset was used to train several DL architectures, to identify the best-performing model (InceptionV4) employed for experiments with generated data. Six training scenarios were tested in InceptionV4 by varying the ratio of real and synthetic data, with evaluation performed on a test set composed of real images using the F1-score and mean Intersection over Union (mIoU) metrics. Results show that training on synthetic data plus a modest addition of 20% real data achieves results comparable to training on real data only. Moreover, the 20/80 scenario (synthetic/real) achieved an 76% F1-score and 80% mean IoU, outperforming the real-only case. As can be seen, the method demonstrates the potential of synthetic data to reduce collection efforts while enhancing crack detection accuracy.
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