School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China.
Fujian Key Laboratory of Digital Simulations for Coastal Civil Engineering, Department of Civil Engineering, Xiamen University, Xiamen 361005, China.
Sensors (Basel). 2022 Aug 26;22(17):6437. doi: 10.3390/s22176437.
The Convenient and accurate identification of the traffic load of passing vehicles is of great significance to bridge health monitoring. The existing identification approaches often require prior environment knowledge to determine the location of the vehicle load, i.e., prior information of the road, which is inconvenient in practice and therefore limits its application. Moreover, camera disturbance usually reduces the measurement accuracy in case of long-term monitoring. In this study, a novel approach to identify the spatiotemporal information of passing vehicles is proposed based on computer vision. The position relationship between the camera and the passing vehicle is established, and then the location of the passing vehicle can be calculated by setting the camera shooting point as the origin. Since the angle information of the camera is pre-determined, the identification result is robust to camera disturbance. Lab-scale test and field measurement have been conducted to validate the reliability and accuracy of the proposed method.
方便、准确地识别过往车辆的交通荷载对桥梁健康监测具有重要意义。现有的识别方法通常需要预先了解环境知识来确定车辆荷载的位置，即道路的先验信息，这在实际应用中不太方便，因此限制了其应用。此外，在长期监测的情况下，摄像机干扰通常会降低测量精度。本研究提出了一种基于计算机视觉的识别过往车辆时空信息的新方法。建立了摄像机与过往车辆之间的位置关系，然后通过将摄像机拍摄点设置为原点来计算过往车辆的位置。由于摄像机的角度信息是预先确定的，因此识别结果对摄像机干扰具有鲁棒性。已经进行了实验室规模的测试和现场测量，以验证所提出方法的可靠性和准确性。
