With the development of drone technology, its application in intelligent scenic areas provides a new solution for tourist flow monitoring. To enhance detection accuracy and satisfy real-time demands, this study proposed a low-altitude target detection algorithm of intelligent scenic areas based on improved YOLOv10, and developed an intelligence scenic areas tourist flow monitoring and statistic system accordingly. By introducing the Large Separable Kernel Attention (LSKA) mechanism, the algorithm optimizes the Spatial Pyramid Pooling Fast (SPPF) module and effectively capturing long-range dependencies in images. In addition, we added a Small Target Detection Layer(STDL) to the YOLOv10 network structure to retain more location information and detailed features about small targets. Results from experiments conducted on the VisDrone2019 dataset show that, compared to the original YOLOv10 model, the enhanced version demonstrates an improvement in Recall by 2.0% and an increase in mAP@0.5 by 1.7%. Compared with other mainstream models, our proposed algorithm has improved on many evaluation metrics, and fulfills the requirements for real-time detection. It has been successfully applied to Tsingtao Beer Museum and has achieved good results. The results of the experiments indicate that the algorithm performs well in detecting low-altitude aerial photography images of drones, and provides effective technical assistance for the safety management of intelligent scenic areas.
With the growth of the aging population, the incidence of eye diseases is getting higher and higher. Traditional manual diagnosis has strong subjectivity and limitations. Computer-aided diagnosis can improve the accuracy of diagnosis while accelerating the diagnosis. The traditional convolutional neural network cannot fully obtain the effective features of the image, which makes the classification accuracy of the image low. The computer-aided diagnosis algorithm proposed in this paper integrates DenseNet and Squeeze-and-Excitation Networks (SENet) in deep learning based on image de-watermarking and data enhancement, while fully extracting and utilizing fundus images features while improving the network's global features information utilization. The experimental results show that the classification accuracy of the model in the fundus image is 0.9528. Compared with other convolutional networks, SEDenseNet achieves the highest accuracy.
Skin diseases not only endanger physical health but also cause psychological problems. Traditional manual diagnosis has strong subjectivity and limitations. Recently, the use of computer-aided diagnosis technology based on deep convolutional neural networks to classify and recognize dermatological images has been widely used. In order to further improve the classification effect, we propose a method to merge the SENet network with the Inception-v4 network. By comparing the DensenNet-121, VGG-16, and ResNet-101 networks, the effectiveness of the SE-Inception-v4 network is verified, and the SENet network has also verified the effectiveness of model performance improvement. Experimental results show that the improved deep learning algorithm in this paper can improve the accuracy of skin disease image classification and has certain guiding significance for the research and application of computer-aided diagnosis in the medical field.
Based on the pictures of rural housing buildings, the characteristics of housing for the poor are studied, and the appearance of wall is classified by the deep learning method. The degree of poverty is determined by the classification of wall characteristics. Using the transfer learning method, the ResNet101 network is combined with the AdaptNet network to train the house image set. The house pictures are classified using the trained model. Experiments show that the classification accuracy in the recognition of wooden walls and tile walls is improved.
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