Image recognition method for electric meter terminal fault recognition

Abstract

The invention discloses an image recognition method for electric meter terminal fault recognition. The method comprises the steps of establishing a classification network based on deep learning, an electric meter terminal detection network and a configuration matching and fault recognition network; the input of the deep learning classification network is an image, and the output is the model of ammeter terminal equipment; wherein the output of the electric meter terminal detection network is a feature corresponding to each electric meter terminal model; acquiring information characteristics ofeach ammeter terminal panel by using a configuration matching detection method; a deep learning network is used for identification, typical'no-fault 'pictures and'faulty' pictures are sent into the deep learning network to be trained, during identification, pictures to be identified are sent into the network, and the network can output a result of whether faults exist. According to the identification method, priori knowledge of the electric meter terminal is utilized to the maximum extent, and the identification success rate of the algorithm is greatly improved. And necessary fault information is accurately provided, so that field operation and maintenance engineers are helped to quickly locate faults, and the efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of electric meter terminal fault identification, and in particular relates to an image recognition method for electric meter terminal fault identification. Background technique [0002] With the development of the electric power industry, there are more and more electric meter terminal equipment. In the actual operation process, various faults may occur in the electric meter, and there are many types of faults. The conventional operation and maintenance mode cannot guarantee its sustainability efficiently and intelligently. For operation, there is an urgent need to use technical means to improve the intelligent level of meter fault identification and improve the economic benefits of operation. With the continuous popularization and application of computer science in various industries, the use of computer vision technology to assist in the rapid identification of meter fault types can simplify system managem...

AI Technical Summary

Problems solved by technology

The fault alarm display mode of each meter terminal, the display position of the alarm light, and the information displayed on the LCD screen are different, which brings great challenges to visual recognition

[0005] 2. The actual collected images of the electric meter may come from different camera equipment, and the installation height of the electric meter box is different, so the obtained images may be different in illumination, angle of view, background (other electric equipment) and resolution, which may cause a fault in the electric meter. Recognition poses further challenges

However, due to the two problems mentioned above, the area where the fault light is marked in the meter terminal is small. Compared with the meter terminal as a whole, the characteristics are relatively weak, but the accuracy of fault identification is very high.

Images