Object detection based on unmanned aerial vehicle(UAV) platforms is essential for both engineering and research. Complex scale problems in UAV application scenarios require strong regression localization capabilities from target detection algorithms. Nonetheless, due to the constraints of UAV platform, it is difficult to increase accuracy by deepening the network. Therefore, this paper presents an improved YOLOv5 with an attention mechanism, consisting a Convolution-Swin Transformer Block(CSTB) utilizing Swin Transformer as well as a Convolution-block Attention Module(CBAM) to improve network positioning accuracy. In addition, this paper incorporates Bidirectional Feature Pyramid Network(BiFPN) [1], Spatial Pyramid Pooling-Fast(SPPF) and some network components to increase the average precision while maintaining the limited size of the model. Experiments on Visdrone2019 dataset show that the proposed approach can raise the mean Average Precision(mAP) by 5.4% compared to YOLOv5, with only 18% increase in model size.
-The smart grid is evolving rapidly from a relatively isolated environment to an opened one. The adoption of information and communication technologies can make greater connectivity and interoperability between components. However, the increased connectivity also brings the challenge to security. First of all, in allusion to the current study on security threats to smart grid simple and deficient, the paper induces and analyzes it thoroughly, which can also correct some misunderstanding in a certain degree, from the view of system level, services, confidentiality and so on. Secondly, based on the analysis of security threats, the paper constructs the security strategy for smart grid. It consists of four steps, which are discussed detailedly one by one. It can provide the protection effectively. Finally, relevant conclusions are made.
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