In target detection, remote sensing images have characteristics such as complex background, dense target distribution, and small targets, which lead to poor detection results, missed detections and false detection. This paper presents ABSYOLOv5s, a target detection method based on YOLOv5s. In order to overcome the difficulties in detecting small target and prevent missed detection and error detection, a self-attention mechanism with position information encoding is introduced to strengthen the fusion of feature information within the same scale. BiFPN is used in the neck network to better integrate low-level and high-level feature information. In addition, the ShapeIoU loss function proposed by Zhang et al. is applied to make the model in this article concentrate more on bounding box’s shape and scale to improve detection accuracy. This paper conducted a complete experiment on the remote sensing vehicle data set COWC. The experiment shows that all indicators of the improved model have been improved, with the accuracy increased by 0.4%, recall, mAP,mAP@0.5/0.95, have improved by 1.1 per cent, 0.7 per cent, and 0.4 per cent, respectively.
To address the current problems of high energy consumption and insufficient resource utilization in the scheduling process of cloud virtual machines, an optimization algorithm based on combined prediction for virtual machine placement was proposed. Firstly, a combined prediction model consisting of the BPNN, ARIMA model and LSSVM model was constructed in the load prediction phase to make the detection of overloaded hosts and underloaded hosts more accurate. Secondly, the classical PABFD algorithm was improved in the virtual machine placement phase by adding the constraint of remaining resource utilization of the destination host and proposed an optimal adaptation decreasing algorithm based on resource utilization. Finally, the results of simulation experiments in the CloudSim platform showed that the adoption of this virtual machine placement algorithm can effectively reduce energy consumption while guaranteeing the quality of service.
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