Detection of Electric Component Based on Improved Faster-RCNN

In the era of smart grid development, it is an important task to improve the efficiency and intelligence of the grid control system. One of the problems that need to be solved is the tedious and error-prone manual drawing of the CAD design diagram, which requires maintainers to refer to the original electrical diagram for manual drawing. This is a huge workload and is prone to mistakes. To solve this problem, the automatic identification of electrical component in diagrams is particularly critical, and existing methods are inadequate in terms of algorithmic accuracy and robustness. To achieve better automatic detection, we propose an improved Faster-RCNN algorithm to detect electrical components in diagrams. In order to further improve the recognition efficiency for a multiple scales of electrical components, we replaced the original feature extractor VGG-16(Visual Geometry Group 16-layer model) with ResNet-50 as the feature extractor and introduce a feature fusion network based on attention block to strengthen the detection ability of multi-size electrical components. In the RPN network, the k-means++ algorithm is introduced to better generate anchor boxes. To overcome the problem of information loss caused by directly reducing the original grid drawing to the predetermined size, the overlapping sliding window is used to detect the high-resolution electrical diagram.