Adaptive Reflection Detection and Control Strategy of Pointer Meters Based on YOLOv5s

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 5; p. 2562
• Main Authors: Liu, Deyuan, Deng, Changgen, Zhang, Haodong, Li, Jinrong, Shi, Baojun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.02.2023; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Data mining; Deep learning; inspection robot; k-means clustering algorithm; pose transformation; reflection detection; Robots; YOLOv5s; China; Pennsylvania; reflection detection; pose transformation; k-means clustering algorithm; inspection robot; YOLOv5s
• DOI: 10.3390/s23052562
• ISSN: 1424-8220

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.