Fault Identification of Overhead Contact System Steady Ears Based on MFDC-SSD Network
Aiming at the problems of complex application environment of overhead contact system,small size of steady ears,special installation direction,difficult identification,and poor performance of traditional target detection algo-rithms,a new method for detecting defects in steady ears of overhead contact system was proposed based on multi-scale feature fusion dense connectivity network(SSD)model.Firstly,the feature extraction network of SSD model was im-proved by combining DenseNet and Inception modules to share the context information among specific feature layers to enhance the feature extraction capability.Then the multi-scale feature maps for SSD detection were fused using FPN step by step from deep to shallow layers of the network to design a dense connected network model for multi-feature fusion.Fi-nally,fGIoU was used as the edge loss function to optimize the overlap between the real and predicted frames during train-ing.The collected image dataset of steady ears of a section of overhead contact system was detected and recognized.The results show that the steady ear defect detection method designed in this paper can detect the steady ear detachment and loosening under the complex backgrounds of the overhead contact system with strong robustness in images with different angles and brightness.
overhead contact systemdefect identificationsteady earfeature pyramidbounding box loss function
万方数据
维普
