UAV (Unmanned Aerial Vehicle) black flight at low altitude could cause serious safety risks. Consequently, it is crucial to detect and manage low altitude small UAVs. The existing methods of low altitude small UAV detection suffer from problems such as high false alarm rate, and poor real-time performance. In order to solve the above problems, we present a novel approach, named AD-YOLOv5s, to achieve low altitude small UAV detection with high precision and high real-time performance. Firstly, the feature enhancement method is used to expand the dataset. We optimize the model feature fusion, the prediction head structure, and the loss function. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, feature enhancement is performed to improve the detection accuracy. Secondly, the ghost module and depthwise separable convolution are used to reduce the number of parameters of the model, and we propose the method of lightweight design of model to improve the detection speed. Compared with the YOLOv5s model, the experiment result shows that our proposed AD-YOLOv5s model improves the value of mAP by 2.2% and the value of Recall by 1.8%, reduces the value of GFLOPs by 29.9% and parameters by 38.8%, and achieves 27.6 FPS when the proposed model deploy on a low-cost edge computing device (jetson nano).
To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.
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