训练一个地铁隧道衬砌表观病害检测模型需大量标注数据，且病害处于动态演化，利用某时间段数据训练的模型无法保证未来检测的效果，若利用新数据不断重新训练成本更是巨大。因此，提出3阶段主动学习方法，跟随检测周期选择性地筛选训练样本。第1阶段，借助分类模型去除非病害类样本；第2阶段，利用分类概率与交并比(IoU)预测样本难度，选择困难样本；第3阶段，将样本映射至特征空间，并通过几何方法衡量图片间相似度，得到多样性兼代表性样本。实验表明：3阶段主动学习方法选择17%的样本，达到全监督学习精度的94.0%，有效减少了标注成本。

Supervised learning-based visual models are effective for tunnel surface disease detection, but model training requires large datasets. Additionally, diseases evolve dynamically, and models trained on data from a specific period may perform well currently but cannot guarantee future detection accuracy. Continuously retraining models with new data incurs significant costs. To address this, a three-stage active learning method is proposed to reduce annotation costs through selective labeling and continuously select new training samples to update the model, ensuring its performance throughout its service life. In the first stage, a classification model selects disease samples based on global feature differences and removes interference from non-disease samples. In the second stage, classification probability and Intersection over Union (IoU) are used to predict sample difficulty, selecting those with higher difficulty for annotation to improve detection accuracy. In the third stage, samples are mapped to feature space, and image similarity is measured using geometric methods to obtain diverse, representative samples, upgrading disease features and reducing redundancy. Experimental results show that selecting 17% of the samples for annotation in the three-stage active learning method achieves 94.0% accuracy for the trained model when annotating all target class samples, ensuring accuracy while reducing annotation costs.