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Analysis of Key Technologies of Bridge Damage Detection Based on Visual Recognition

机译：基于视觉识别的桥梁损伤检测关键技术分析

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In the identification of bridge damage in the series of deflection-affecting lines, noise signals due to axle coupling often occur. Removing these noise signals has become a key technique for effectively identifying damage. Taking the main line bridge of a overpass project in Fuzhou City of Fujian Province as an example, we collected the deflection a data of the bridge by using HPON-X target-free bridge deflectometer and used YOLOv3 algorithm for deep learning of the vehicle load position. The data were measured and studied by using DB9 wavelet de-noising method. The research shows that this method can greatly reduce the influence of vehicle bridge interaction on deflection influence line, and can enhance the accuracy and speed of bridge damage detection.
机译：在偏转冲击线系列中的桥梁损坏的识别中，经常发生由于轴耦合引起的噪声信号。 去除这些噪声信号已成为有效识别损坏的关键技术。 以福建省福州市福州市立交桥主桥为例，我们通过使用HPON-X目标无桥偏转计来收集桥梁的数据，并使用YOLOV3算法进行深度学习车载位置 。 通过使用DB9小波去噪法测量和研究数据。 研究表明，该方法可以大大降低车辆桥梁相互作用对偏转影响线的影响，可以提高桥梁损伤检测的准确性和速度。

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