Abstract: Vectorization of engineering drawings plays a key role in the digital foundation of power grid. Due to the variety of electrical components in power grid, some image backgrounds are blurred, and the rotation angle of the electrical components is not consistent, which poses a challenge for the identification of the electrical elements in the drawings. This paper mainly studies the electrical element recognition and training with the Faster RCNN network architecture in deep learning, and performs preprocessing and feature extraction on the images to be trained, including preprocessing in smooth denoising, binarization, segmentation, etc. The feature extraction uses VGG16 network, and then uses Faster RCNN for classification. Experimental results on real-world datasets with 9 categories of electrical circuit elements show that the performance of detection and classification of electrical elements are efficient.

Key words: electrical circuit element, Faster RCNN, object detecting