State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, China.
Hebei Key Laboratory of Robot Sensing and Human-Robot Integration, Tianjin 300401, China.
Sensors (Basel). 2023 Feb 25;23(5):2562. doi: 10.3390/s23052562.
Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the movement of inspection robots. The proposed detection method has the potential application to realize real-time reflection detection and recognition of pointer meters for inspection robots in complex environments.
在复杂环境中，检测机器人对指针式仪表进行检测时常常会出现反射现象，导致指针式仪表读数失败。为此，本文提出了一种基于深度学习的指针式仪表反射区域自适应检测的改进 K 均值聚类方法和机器人位姿控制策略来消除反射区域。该方法主要包括三个步骤：（1）利用 YOLOv5s（You Only Look Once v5-small）深度学习网络实时检测指针式仪表，采用透视变换对检测到的反射指针式仪表进行预处理，然后将检测结果与深度学习算法相结合，进行透视变换；（2）基于采集到的指针式仪表图像的 YUV（亮度-带宽-色度）颜色空间信息，得到亮度分量直方图的拟合曲线及其峰谷信息，然后基于该信息对 K 均值算法进行改进，自适应确定其最佳聚类数及其初始聚类中心，并基于改进的 K 均值聚类算法进行指针式仪表图像的反射检测；（3）确定机器人位姿控制策略，包括移动方向和距离，以消除反射区域。最后，搭建了检测机器人实验平台，对所提检测方法的性能进行了实验研究。实验结果表明，该方法不仅具有较高的检测精度（达到 0.809），而且检测时间最短，与文献中的其他方法相比，检测时间仅为 0.6392s。本文的主要贡献在于为检测机器人提供了一种避免周向反射的理论和技术参考，可以自适应、准确地检测指针式仪表的反射区域，并通过控制检测机器人的运动快速消除反射区域。所提出的检测方法具有潜在的应用价值，可以实现检测机器人在复杂环境下对指针式仪表的实时反射检测和识别。
