基于YOLO v5的古建筑木结构裂缝检测方法

Research on Crack Detection Method of Wooden Ancient Building Based on YOLO v5

目的针对古建筑木结构裂缝检测效率低、成本高、周期长的缺点,提出利用YOLO v5对古建筑木结构裂缝实现智能检测。方法首先,利用佳能照相机采集古建筑木结构裂缝图片,建立Pascal VOC数据集;其次,基于Facebook开发的Pytorch深度学习框架,用数据集对YOLO v5进行训练,同时分析各项性能参数指标;最后,以沈阳建筑大学校园内八王寺为例,进行裂缝识别验证。结果在迭代350次的情况下,训练损失可以降到0.042,AP值达到0.918,裂缝检测精度达到91%左右。结论笔者利用YOLO v5目标检测方法可以快速、准确的识别出古建筑木结构的裂缝,相比较传统的人工检测方法具有高效、便捷、成本低的优点。

We proposed a crack decoction method based on YOLO v5 for the purpose of increasing efficiency,cutting costs and short the time of the crack detection.Firstly,we created a Pascal VOC dataset based on the wooden ancient building photos we have taken by a Canon camera;Then we trained the dataset with YOLO v5 based on the Pytorch which was an open source machine learning framework supported by Facebook;Finally,we tested the method we proposed with a famous building named Bawang temple.The test results shows that after 350 times trained the model,the value of loss function was 0.042 and the AP value was 0.918,the precision of the model was 91%.The experimental results verify that it is feasible to analyze crack by YOLO v5 with faster and less waste than the traditional way.