基于DC-YOLO模型的建筑物砌体构件危险性等级鉴定方法

HAZARD LEVEL IDENTIFICATION OF BUILDING MASONRY COMPONENTS BASED ON DC-YOLO MODEL

为了提高房屋危险性等级鉴定工作的效率,避免人为干预,使鉴定结果更客观,设计DC-YOLO模型。该模型是将深度学习与目标检测算法结合,设计的一种砌体构件危险性等级自动化鉴定方法。采用K-means聚类获得最佳先验框数量和尺寸;扩大网格尺寸以提高对小目标的识别能力;引入可变形卷积,以适应砌体构件裂缝形状不规则的特点。实验结果表明,DC-YOLO模型与常规方法比较,对各类目标的识别率均有不同程度的提高,精确率、召回率、F1值均达到了较好的效果,对于建筑物砌体构件危险性等级有较好的分类性能。

In order to improve the efficiency of the house hazard level identification, avoid human intervention, and make the identification results more objective, the DC-YOLO model was designed. By combining deep learning with target detection algorithms, the model was an automated identification method for the hazard level of masonry components. K-means clustering was used to obtain the best number and size of a priori frames. The grid size was expanded to improve the ability to identify small targets. We introduced deformable convolution to adapt to the irregular shape of the cracks in masonry components. The experimental results show that compared with the conventional method, DC-YOLO model has improved the recognition rate of various targets in varying degrees. The precision ratio, recall and F1 value have achieved good results. It has better classification performance for the hazard level grade of building masonry components.