Considering that current intrusion detection methods have low accuracy and long detection time when network traffic is classified, an improved neural network intrusion detection method based on support vector machine is proposed. Convolutional neural network extracts network traffic locally and deeply, and bidirectional gated recurrent unit extracts network traffic time sequence features. The two methods are combined to form a comprehensive feature extraction method with temporal memory function. Finally, the extracted features are classified by support vector machine instead of SoftMax activation function. According to the experimental results on the NSL-KDD dataset, the accuracy of CNN-BiGRU-SVM model is 99.67%, which is 25.35% higher than that of CNN-BiGRU-SoftMax model, effectively improving the accuracy of network traffic detection.
Drones have been used in many practical applications,,such as agriculture, aerial photography and surveillance. Therefore, it is very difficult for machines to automatically understand the visual data collected by drones, which makes the connection between computer vision and drones become inseparable. However, the identification of targets from aerial images is faced with the difficulty that the target to be detected is usually too small, too dense, and the relative size is not fixed relative to the image, and the relative motion of drone and traffic leads to the blurred target. To address these challenges, this paper proposes an improved YOLOv5 network model. Based on YOLOv5, we integrate the convolution block attention model ( CBAM ) and the attention mechanism of Swin Transformer to enable it to effectively focus on the attention area in dense small object scene and reduce the calculation amount. We also adopt the BiFPN structure for the structure of the neck network, which can obtain more effective feature information and reduce some unnecessary connections. In addition, we also add an additional detector to detect small objects. To further improve our YOLOv5, we adopt the data enhancement strategy. Extensive experiments on the VisDrone 2019 dataset show that the The AP result of the improved YOLOv5 was 32.83 %. Compared with the baseline model ( YOLOv5 ), YOLOv5 increased by about 6.5 %, indicating that the improved model was effective.
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