裂缝检测模型数据集的低监督快速标注算法

Fast Labeling Algorithm of Crack Detection Dataset with Low Supervision

摘要:

为解决裂缝检测深度学习模型训练时数据集标注效率低、成本高的现状，以及现有计算机标注算法对复杂环境适应性较弱的问题，基于计算机视觉与概率统计理论，提出低监督快速标注的概念，并以计算机标注和人工标注相融合的全新标注模式，形成了完整的裂缝检测模型数据集的快速标注算法。与人工逐像素标注相比，标注精度均为84%以上，且可节省至少85%的时间；与传统计算机标注方式相比，标注干涉和简单人工标注方式可以较好地处理复杂背景问题。经U?Net深度学习模型验证，测试集的平均交并比可达0.90。

Abstract:

Manual dataset labeling is always at the expense of low efficiency and high cost in the deep learning model training of crack detection. The adaptability of existing automatic labeling algorithms to complex environment is weak as well. Aiming at these problems， the concept of fast labeling with low supervision is proposed based on computer vision and the probability statistics theory. In addition， a novel fast labeling algorithm for crack dataset is formed， composed of computer labeling and manual labeling. Compared with manual pixel-by-pixel labeling， the labeling accuracy of the proposed automatic method is more than 84%， and can save at least 85% of the time. Compared with traditional computer labeling， complex background can be better dealt with by labeling interference and simple manual labeling. Validated by U-Net deep learning model， the average intersection ratio of test sets can reach 0.90.