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With the rapid development of the power grid, the site construction and operation environment is more extensive and complex, the installation work of the electric energy metering device is heavy, and it is easy to not regulate, and it is very difficult for managers to conduct real-time and effective supervision and testing. Therefore, combined with the successful application of deep learning in target detection, we propose a measuring device component detection method based on deep learning SSD model to detect the installation quality, saving time and manpower. The experimental results show that our method can ensure the detection accuracy and speed, which is of great significance for the implementation and monitoring of power system installation specification. Introduction With the continuous development of smart grid, the popularization of electric energy metering devices is also more and more widespread. As an energy metering device, whether the installation is standardized is of great significance to accurate statistics of users and relevant departments. Although the relevant departments have set exact standards for the installation, it may not be fully compliant due to the complex environment at the construction site. In addition, the workers in different circumstances are difficult to comply with the installation of a uniform standard. In the meantime, due to the large amount of installation work, managers cannot conduct real-time and effective supervision and testing. Therefore, it is necessary to adopt a suitable method to check whether the quality of the on-site installation process of the metering device is in accordance with the specifications. We can judge the installation quality by detecting the energy meter, the collection terminal and the test junction box which are typical. Although these devices have obvious characteristics such as color, shape and relative position, due to the interference information is too large, large amounts of image data and other reasons, to ensure the speed and accuracy of detection poses a greater challenge, making detection more difficult. In recent years, with the continuous maturation of deep learning technologies, it has brought tremendous development prospects in the field of image processing. Since 2012, Hinton's team has achieved an overwhelming success using the Deep Convolutional Neural Network (CNN) algorithm at the Image Net large-scale visual identity challenge, a thriving deep learning wave has emerged in computer vision field and has achieved great success so far. In the aspect of target detection [1], deep learning represents the most advanced level currently. The accuracy and speed of target detection have reached a new height from R-CNN, SPP-Net, Fast-RCNN, Faster-RCNN to YOLO, SSD, which has a very good effect to solve the problem of the above components detection. In view of this, this paper applies the successful method of target detection to the component detection of measuring devices, based on the deep learning SSD model, combined with the characteristics of measuring device, constructs a fast and accurate detection method of components with high performance.

The Component Detection of Electric Energy Measurement Device Based on SSD Model