School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2022 Nov 2;22(21):8425. doi: 10.3390/s22218425.
As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene "overlapping" with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value.
作为高速铁路（HSR）从外部获取电力的重要设备，受电弓的状态将直接影响 HSR 的运行安全。为了解决当前受电弓检测方法易受环境影响、无法有效应对外部场景干扰、准确率低、难以满足 HSR 实际运行要求的问题，本研究提出了一种受电弓检测算法。该算法主要包括三部分：第一部分是使用仅看一次（YOLO）V4 实时检测和定位受电弓区域；第二部分是针对直接影响高速摄像机（HSC）的外部干扰的模糊和污垢检测算法，导致受电弓未被检测到；最后一部分是在成像时与受电弓“重叠”的外部复杂场景的复杂背景检测算法，导致受电弓无法有效识别。结合斑点检测和改进的 Brenner 方法的污垢和模糊检测算法可以准确评估 HSC 的污垢或模糊程度，基于灰度和垂直投影的复杂背景检测算法可以大大减少 HSR 运行期间的外部场景干扰。本研究提出的算法在大量 HSR 运行视频样本上进行了分析和研究，在三个不同测试样本上的精度分别达到 99.92%、99.90%和 99.98%。实验结果表明，本研究提出的算法具有较强的环境适应性，能够有效克服复杂背景和外部干扰对受电弓检测的影响，具有较高的实际应用价值。
