Aiming at the problems of low building segmentation accuracy and blurred edges in high-resolution remote sensing images, an improved fully convolutional neural network is proposed based on the SegNet network. First, GELU, which performs well in deep learning tasks, is selected as the activation function to avoid neuron deactivation. Second, the improved residual bottleneck structure is used in the encoding network to extract more building features. Then, skip connections are used to fuse images The low-level and high-level semantic features are used to assist image reconstruction. Finally, an improved edge correction module is connected at the end of the decoding network to further correct the edge details of the building and improve the edge integrity of the building. Experiments are carried out on the Massachusetts building dataset, and the precision rate, recall rate, and F1 value reach 93.5, 79.3, and 81.9, respectively, and the comprehensive evaluation index F1 value is improved by about 5 compared with the basic network.

A semantic ontology-driven hierarchical consistency segmentation algorithm was proposed to solve the segmentation inconsistency of animation character models because of the changing of poses. The mapping between semantic labels and local geometric features was extracted to form a segmentation ontology. In the process of segmentation, support vector machine (SVM) and local geometric features were used to identify the semantic labels, and segmentation was carried out according to the semantic label driving hierarchy to ensure the consistency of the segmentation levels of animation character models. Poisson equation was used to define contour lines for the equal perimeters of segmentation boundary because of the changing of poses. This optimization method made the segmentation boundary smooth and consistent under the changing of poses. In the experiment part, all kinds of animation character models under different poses were verified and analyzed, and the consistent hierarchical segmentation effect was obtained. Compared with the existing methods, the proposed segmentation ontology can solve the problem of adaptive selection of optimization parameters of different classes of models, and improve the segmentation quality. With the continuous development of deep learning, the use of image segmentation for animation, and human pose recognition will become more and more important.

To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is proposed. Firstly, based on the rotating target detection algorithm R-centernet, the Ghost ResNet network is constructed to reduce the number of model parameters by replacing the traditional convolution in the backbone network with Ghost convolution. Secondly, a channel attention module integrating width and height information is proposed to enhance the network’s ability to accurately locate salient regions in folk light images. Content-aware reassembly of features (CARAFE) up-sampling is used to replace the deconvolution module in the network to fully incorporate feature map information during up-sampling to improve target detection. Finally, the constructed dataset of rotated and annotated light and shadow SAR images is trained and tested using the improved R-centernet algorithm. The experimental results show that the improved algorithm improves the accuracy by 3.8%, the recall by 1.2% and the detection speed by 12 frames/second compared with the original R-centernet algorithm.