5results about How to "Efficient and accurate classification" patented technology

The application provides a ConvNeXt model SAR ship classification method based on multi-level feature cooperative interaction. In order to effectively utilize traditional manual features, the method uses a Canny-based edge detection method, captures edge information of objects in an image through multi-level filtering and edge gradient analysis, and inputs the result into a ConvNeXt model for feature extraction. In addition, a multi-level branch structure is expanded in the ConvNeXt model, which is used for deep extraction of feature information at different scales, so as to capture various information of the image at low-level features and high-level features. Finally, cross fusion with traditional manual features is carried out in the multi-scale feature extraction process, so as to improve the classification accuracy of the model.

This invention relates to the field of unmanned aerial vehicle (UAV) inspection technology, specifically an intelligent analysis system for UAV river and lake inspection in smart water conservancy. The system includes a UAV equipped with visible light and thermal infrared sensors performing a dual-temporal detection mission to capture images and data under different lighting and heat conditions. In the anomaly identification stage, a generative neural network trained on normal river and lake images is used to initially locate anomalies by calculating the error between real-time images and model-reconstructed images. A water surface reflection suppression network is introduced for secondary processing to filter out specular reflection artifacts and generate candidate anomaly areas. Anomaly diagnosis employs a two-level strategy: first, a rapid coarse classification is performed by calculating apparent thermal inertia values ​​based on dual-temporal thermal infrared data; then, a secondary fine-tuning diagnosis is performed by combining depth texture and higher-order spectral moment analysis to achieve accurate classification of anomaly types. This invention enables precise and automated identification and early warning of river and lake anomalies through UAV inspection.

The application relates to an automatic classified biomass garbage recycling treatment system for a zero-carbon park, which comprises a garbage classification system, a garbage treatment system, a storage system and a cleaning system. The garbage classification system classifies and processes garbage, the garbage treatment system recycles and processes garbage, the storage system stores the recycled and processed garbage, and the cleaning system cleans the garbage classification system. The automatic classified biomass garbage recycling treatment system for the zero-carbon park can solve the problems that garbage classification often depends on manual work or single technology, the classification accuracy is low, a large amount of garbage is still landfilled or incinerated, energy is wasted, carbon emission is caused, especially the biomass waste such as peels and fallen leaves generated in the park, which needs to be collected and transported to the outside for traditional treatment, thereby increasing traffic energy consumption and carbon footprint.

The application provides a fabric pilling automatic grading method and device, and relates to the technical field of textile detection. The method comprises the following steps: collecting a thermal infrared polarization image sequence of a fabric sample by using a thermal infrared polarization camera; calculating a polarization degree and a thermal decay time constant based on the sequence data, and extracting a micro-thermal radiation feature; simultaneously collecting a visible light image by using a visible light camera, and extracting an image feature; fusing the micro-thermal radiation feature and the image feature to generate multi-modal feature data; and finally identifying and determining a pilling grade by using a neural network model. By combining thermal physical characteristics and visual features, the problems of strong subjectivity of artificial grading and large limitation of single optical detection are overcome, and objective, accurate and efficient automatic grading is realized.

The application discloses a virus-host RNA sequence classification method and device based on AI, and relates to the field of biological detection.The method comprises the following steps: mapping preprocessed short read RNA sequences to a host genome twice, assembling short read RNA sequences which are not mapped to the host genome into continuous RNA sequences, and screening RNA sequences with a length greater than 1000bp from the continuous RNA sequences; and performing AI classification on the RNA sequences with a length greater than 1000bp to obtain virus RNA sequences.The application combines host filtering, rapid assembly and AI classification, can significantly reduce the calculation amount and hardware pressure, realizes efficient and accurate virus sequence classification, and can quickly distinguish unknown viruses.