Abstract：
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.
摘要：
检测机器人在复杂环境下对指针式仪表的检测过程中经常会出现反射现象,这可能导致指针表读数失败。在本文中,提出了一种改进的k-means聚类自适应检测指针表反射区域的方法和一种基于深度学习的机器人姿态控制策略。它主要包括三个步骤：（1）YOLOv5s（YouOnlyLookOncev5-small）深度学习网络用于指针仪表的实时检测。通过使用透视变换对检测到的反射指针仪表进行预处理。然后，将检测结果和深度学习算法与透视变换相结合。(2)基于采集的指针表图像的YUV(亮度-带宽-色度)颜色空间信息,得到亮度分量直方图及其峰谷信息的拟合曲线。然后，k-means算法根据该信息进行改进,自适应地确定其最优聚类数和初始聚类中心。此外,基于改进的k-means聚类算法对指针仪表图像进行反射检测。(3)机器人位姿控制策略,包括它的移动方向和距离，可以确定消除反射区域。最后,搭建了检测机器人检测平台,对所提检测方法的性能进行了实验研究。实验结果表明,该方法不仅具有较好的检测精度,达到0.809,而且检测时间短,与文献中可用的其他方法相比，仅为0.6392s。本文的主要贡献是为检测机器人避免周向反射提供了理论和技术参考。它可以自适应和准确地检测指针仪表的反射区域，并可以通过控制检测机器人的运动来快速去除它们。该检测方法对于实现复杂环境下巡检机器人指针仪表的实时反射检测和识别具有潜在的应用价值。
