In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.T...In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.To address these issues,we collect and annotate the substation rotated device dataset(SRDD).Further,feature fusion and feature refinement network(F3RNet)are constructed based on the classic structure pattern of backbone-neck-head.Considering the similar appearance of electrical devices,the deconvolution fusion module(DFM)is designed to enhance the expression of feature information.The balanced feature pyramid(BFP)is embedded to aggregate the global feature.The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices.It can generate more accurate feature vectors.To address the problem of sample imbalance between electrical devices,the gradient harmonized mechanism(GHM)loss is utilized to adjust the weight of each sample.The ablation experiments are conducted on the SRDD dataset.F3RNet achieves the best detection performance compared with classical object detection networks.Also,it is verified that the features from global feature maps can effectively recognize the similar and small devices.展开更多
基金This work was supported by Science and Technology Project of State Grid Corporation of China(Research and application of audiovisual active perception and collaborative cognitive technology for smart grid operation and maintenance scenarios)(5600–202046347 A-0–0–00).
文摘In modern energy systems,substations are the core of electricity transmission and distribution.However,similar appearance and small size pose significant challenges for automatic identification of electrical devices.To address these issues,we collect and annotate the substation rotated device dataset(SRDD).Further,feature fusion and feature refinement network(F3RNet)are constructed based on the classic structure pattern of backbone-neck-head.Considering the similar appearance of electrical devices,the deconvolution fusion module(DFM)is designed to enhance the expression of feature information.The balanced feature pyramid(BFP)is embedded to aggregate the global feature.The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices.It can generate more accurate feature vectors.To address the problem of sample imbalance between electrical devices,the gradient harmonized mechanism(GHM)loss is utilized to adjust the weight of each sample.The ablation experiments are conducted on the SRDD dataset.F3RNet achieves the best detection performance compared with classical object detection networks.Also,it is verified that the features from global feature maps can effectively recognize the similar and small devices.
