To enhance the precision of detecting small targets in remote sensing images, a target detection algorithm based on improved faster R-CNN is proposed in this paper. In order to enhance the ability of feature extraction for image object category, the algorithm uses the large network model EfficientNet in the backbone feature extraction network. introduce an improved FPN (feature pyramid network) model to reduce that loss of key information of small target caused by the deep neural network, enhancing the capability of extracting feature information of the small target of the fast R-CNN model to the remote sensing image, and being capable of better coping with the remote sensing image with complex background and drastic size change, thereby reducing the omission rate. Finally, CBAM attention module is introduced into the feature graph output of the network model to enhance the interest of the model in feature information and improve the detection ability. Experiments on the NWPU_VHR-10 dataset show that the proposed algorithm improves the accuracy by 8.82%.
This paper proposes a new model, PGB_GPT, for Chinese fine-grained sentiment analysis, which combines Bidirectional Long Short-Term Memory (BiLSTM), Graph Convolutional Neural Network (GCN), and Generative Pre-Training Model (GPT). Additionally, a multi-core plant intelligent model is introduced to extract comprehensive symbolic meaning and improve the precision and accuracy of sentiment analysis. PGB_GPT outperforms other combination models and the possibility of merging a multi-core plant intelligence model with BiLSTM, GCN, and GPT for more extensive and accurate emotion analysis is highlighted. For Chinese fine-grained sentiment analysis, the PGB_GPT model combines BiLSTM, GCN, and GPT, with "P" representing "Plant Intelligence," "G" representing "Graph Convolutional Neural Network," "B" representing "Bidirectional Long Short-Term Memory," and "GPT" representing "Generative Pre-Training Model." As evidenced by the sentiment analysis dataset evaluation, each component greatly contributes to the model's enhanced performance.
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