Improved SSD Detection Method for Substation Power Equipment
The detection of substation equipment based on machine vision is a key link in the intelligent inspection of electric power.The size of different equipment in the inspection images is significantly different,which greatly affects the detection perfor-mance of the existing algorithms.Aiming at the detection objects with significant size differences,an improved SSD algorithm is pro-posed for substation equipment detection tasks.This method mainly improves two important parts of the SSD algorithm.First,it en-sures the extraction of multi-scale features and strengthens the effectiveness of large-scale feature maps.Second,it optimizes the generation strategy of the default frame,and proposes a variety of default frames that meet the objects with significant differences in size.In addition,in order to enhance the robustness of the network,transfer learning technology and data enhancement technology are used to train the power equipment detection network model.Finally,experiments are carried out on the data set of power equip-ment in substations,and the method obtains the best accuracy and recall rate,which shows that it can better solve the problem of de-tecting objects with significant size differences.
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