基于关键点检测的钢桥螺栓松动识别方法

BOLT-LOOSENING IDENTIFICATION METHOD OF STEEL BRIDGES BASED ON KEY POINT DECTION

针对钢桥高强螺栓人工检测效率低、风险大,难以量化松动程度等问题,研发了一种基于关键点识别的批量螺栓松动检测方法.首先,通过卷积神经网络模型定位图像中的螺栓关键点;其次,采用K-means聚类算法对螺栓关键点进行包络,计算螺栓初始角度与松动角度.通过收集试验室环境下的螺栓图片对算法性能进行验证,结果表明试验室松动测试均方根误差均在0.63°-1.83°,最大误差为2°,单张平均检测耗时仅为51 ms.方法识别的松动角度与实际松动角度非常吻合,测试误差满足工程应用要求.

Aiming at low efficiency,high risk,and difficulty in quantifying the degree of loosening of high-strength bolts in steel bridges,a batch bolt loosening detection method based on key point identification is proposed.Firstly,the key points of bolts in the image are located by the convolutional neural network model,then the K-means cluster-ing algorithm is used to envelope the key points of the bolt,and the initial angle and loosening angle of the bolt are calculated.The performance of the algorithm is verified by collecting bolt images in the laboratory.The results show that the root mean square error of the loosening test in the laboratory is 0.63°~1.83°;the maximum error is 2°,and the average detection time is only 51 ms.The loosening angle identified by this method is very consistent with the ac-tual loosening angle,and the test error meets the requirements of engineering application.