用少量样本实现裂缝图像精准分类，提升工程检测效率。
Few-shot crack image classification using clip based on bayesian optimization
- 结合CLIP与贝叶斯优化，利用多模态信息进行小样本分类
- 在极小标注数据下仍保持高准确率，泛化能力显著增强
- 适合缺乏大量标注数据的土木工程裂缝检测场景
本研究提出一种基于CLIP与贝叶斯优化的新型少样本裂缝图像分类模型。通过融合多模态信息与贝叶斯优化方法，该模型可在少量训练样本下实现高效裂缝图像分类。CLIP模型利用其强大的特征提取能力，支持在样本有限情况下实现精确分类；而贝叶斯优化则提升了模型的鲁棒性与泛化性能，降低对大规模标注数据的依赖。实验结果表明，该模型在多种数据集规模下均表现出稳健性能，尤其在小样本场景中优势明显，验证了该方法在土木工程裂缝分类中的应用潜力。
原文摘要 · Abstract (English)
This study proposes a novel few-shot crack image classification model based on CLIP and Bayesian optimization. By combining multimodal information and Bayesian approach, the model achieves efficient classification of crack images in a small number of training samples. The CLIP model employs its robust feature extraction capabilities to facilitate precise classification with a limited number of samples. In contrast, Bayesian optimisation enhances the robustness and generalization of the model, while reducing the reliance on extensive labelled data. The results demonstrate that the model exhibits robust performance across a diverse range of dataset scales, particularly in the context of small sample sets. The study validates the potential of the method in civil engineering crack classification.
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