In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper.
With the introduction of software‐defined networking (SDN) into wireless sensor networks (WSNs) to simplify network management and increase agility, software‐defined WSNs (SDWSNs) came into being. Since SDN adopts an idea of centralized control, reliable control is a necessary guarantee of its performance. However, in SDWSN, the unreliable wireless links pose a significant challenge to the reliable control of data plane. In view of this, this paper adopts the idea of separating the forwarding process of control flow and data flow and presents a light‐weight control model for data plane in SDWSN. Based on the network global view, the model constructs an independent control forwarding layer to route control flow. The light‐weight feature of this model lies in that there is no need to deploy additional control networks. Finally, we design a fast update algorithm for the control failure that may occur by searching for the replacement of neighboring control nodes and achieve efficient maintenance of the control forwarding layer. The simulation results show that the proposed model has shorter request‐response time than the original SDN‐WISE, and the impact on data plane forwarding performance is also within the acceptable range.
Positioning is the basic function of wireless sensor networks (WSN). At present, range-based positioning is a common method to obtain the position of a node, but its accuracy has encountered a bottleneck. The fundamental reason is that it ignores the ranging information between blind nodes. Therefore, based on the design of the positioning optimization factor to adjust the weight of the ranging information between the blind nodes, the positioning model of minimizing the error is established. On the basis of designing the blind node positioning matrix, the multiblind node localization problem is transformed into a single-objective optimization task, and the model also supports two-dimensional and three-dimensional positioning. In order to solve the model efficiently, we added a self-adaptive function that matches the positioning requirements for the explosion search mechanism of the fireworks algorithm (FWA) and then proposed a self-adaptive FWA (SA-FWA). The experimental results on the real ranging dataset show that the model has higher accuracy than other methods and achieves the current optimal positioning error, which is 1.88 m for received signal strength data and 1.02 m for time of arrival information, respectively.
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