Based on the machine vision and artificial intelligence technologies, the reading and acquisition of meter values have become the mainstream technical solutions for real-time monitoring of production data in the current industrial field. At the same time, the number of intelligent instruments and meters is increasing, and the quality varies. There is a lack of scientifically measurement methods and quality trust for intelligent meter reading algorithms. To promote their reliable and widespread application in various fields, it is necessary to conduct in-depth research on the assessment system of artificial intelligence in intelligent meter reading systems. This paper comprehensively studies various aspects involved in the emerging measurement field of artificial intelligence evaluation at the current stage, providing scientific measurement and evaluation references for the quality trust of artificial intelligence in the field of meters.

Image feature description and matching is widely used in computer vision, such as camera pose estimation. Traditional feature descriptions lack the semantic and spatial information, and give rise to a large number of feature mismatches. In order to improve the accuracy of image feature matching, a feature description and matching method, based on local semantic information fusion and feature spatial consistency, is proposed in this paper. Once object detection is used on images, feature points are then extracted, and image patches with various sizes surrounding these points are clipped. These patches are sent into the Siamese convolution network to get their semantic vectors. Then, semantic fusion description of feature points is obtained by weighted sum of the semantic vectors, and their weights optimized by particle swarm optimization (PSO) algorithm. When matching these feature points using their descriptions, feature spatial consistency is calculated based on the spatial consistency of matched objects, and the orientation and distance constraint of adjacent points within matched objects. With the description and matching method, the feature points are matched accurately and effectively. Our experiment results showed the efficiency of our methods.