用迁移学习+注意力机制+遗传算法优化，精准检测基础设施裂缝
Crack Detection in Infrastructure Using Transfer Learning, Spatial Attention, and Genetic Algorithm Optimization
- 基于ResNet50迁移学习，结合空间注意力与遗传算法调优网络结构
- 在有限数据下实现99.67%精确率和99.83%的F1分数
- 适合缺乏大规模标注数据的实际工程场景使用
裂缝检测对道路、桥梁和建筑等基础设施的维护与安全至关重要，及时发现结构损伤可避免事故并降低维修成本。传统人工巡检耗时费力、主观性强且存在安全隐患。本文提出一种融合深度学习的先进裂缝检测方法，利用迁移学习、空间注意力机制及遗传算法（GA）优化模型结构。为应对大规模数据难获取的问题，采用预训练的ResNet50模型，充分发挥其特征提取能力，减少对海量训练数据的依赖。通过引入空间注意力层，并以遗传算法微调定制化神经网络架构，提升了模型性能。实证研究表明，所提出的Attention-ResNet50-GA模型在测试中达到0.9967的精确率和0.9983的F1分数，显著优于传统方法。结果表明该模型具备在多种复杂条件下准确识别裂缝的能力，尤其适用于标注数据稀缺的真实应用环境。
原文摘要 · Abstract (English)
Crack detection plays a pivotal role in the maintenance and safety of infrastructure, including roads, bridges, and buildings, as timely identification of structural damage can prevent accidents and reduce costly repairs. Traditionally, manual inspection has been the norm, but it is labor-intensive, subjective, and hazardous. This paper introduces an advanced approach for crack detection in infrastructure using deep learning, leveraging transfer learning, spatial attention mechanisms, and genetic algorithm(GA) optimization. To address the challenge of the inaccessability of large amount of data, we employ ResNet50 as a pre-trained model, utilizing its strong feature extraction capabilities while reducing the need for extensive training datasets. We enhance the model with a spatial attention layer as well as a customized neural network which architecture was fine-tuned using GA. A comprehensive case study demonstrates the effectiveness of the proposed Attention-ResNet50-GA model, achieving a precision of 0.9967 and an F1 score of 0.9983, outperforming conventional methods. The results highlight the model's ability to accurately detect cracks in various conditions, making it highly suitable for real-world applications where large annotated datasets are scarce.
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