The automatic extraction of individual tree from mobile laser scanning (MLS) scenes has important applications in tree growth monitoring, tree parameter calculation and tree modeling. However, trees often grow in rows and tree crowns overlap with varying shapes, and there is also incompleteness caused by occlusion, which makes individual tree extraction a challenging problem. In this paper, we propose a trunk-constrained and tree structure analysis method to extract trees from scanned urban scenes. Firstly, multi-feature enhancement is performed via PointNet to segment the tree points from raw urban scene point clouds. Next, the candidate local tree trunk clusters are obtained by clustering based on the intercepted local tree trunk layer, and the real local tree trunk is obtained by removing noise data. Then, the trunk is located and extracted by combining circle fitting and region growing, so as to obtain the center of the tree crown. Further, the points near the tree’s crown (core points) are segmented through distance difference, and the tree crown boundary (boundary points) is distinguished by analyzing the density and centroid deflection angle. Therefore, the core and boundary points are deleted to obtain the remaining points (intermediate points). Finally, the core, intermediate and boundary points, as well as the tree trunks, are combined to extract individual tree. The performance of the proposed method was evaluated on the Pairs-Lille-3D dataset, which is a benchmark for point cloud classification, and data were produced using a mobile laser system (MLS) applied to two different cities in France (Paris and Lille). Overall, the precision, recall, and F1-score of instance segmentation were 90.00%, 98.22%, and 99.08%, respectively. The experimental results demonstrate that our method can effectively extract trees with multiple rows of occlusion and improve the accuracy of tree extraction.

Image deraining is a hot research topic, which aims to remove various rain streaks (raindrops) from rainy images and restore the backgrounds. Though image deraining has been extensively studied in recent years, few methods are able to effectively and efficiently derain real-world high-resolution rainy images. In general, existing image deraining methods are restricted by two main factors while processing high-resolution images. First, the computational complexity and memory usage of existing deep learning-based methods are high when it comes to derain highresolution images. Second, as the image resolution increases, it is difficult to simultaneously extract and aggregate both global and local features for clean rain removal. In this paper, we propose a novel network, called Global-Local Grafting Fusion Network (GLGFN) , for deraining real-world high-resolution images. Our GLGFN utilizes a staggered connection structure to achieve deeper sampling depth while maintaining low computational cost. It adopts the Transformer and CNN based encoders (backbones) to extract global and local features, respectively, and then grafts global features into local features to guide the extraction of rain streaks. In addition, for well fusing global and local features, we also propose a Grafting Fusion Module (GFM) , which adopts Cross Sparse Attention (CSA) and Selective Kernel Fusion (SK Fusion) to efficiently aggregate global and local features. Extensive experiments conducted on several high-resolution real rainy datasets have demonstrated the effectiveness and efficiency of our proposed GLGFN . We will release our code and dataset.

Spatio-temporal action detection networks, which need to simultaneously extract and fuse spatial and temporal features, often result in existing models becoming bloated and difficult to run in real-time and deploy on edge devices. This paper introduces an efficient and real-time spatio-temporal action detection model, YOWOv3. This model uses efficient 3D and 2D backbone networks to separately extract spatial and spatial-temporal features from sequential information. A lightweight spatio-temporal feature fusion module, designed by deeply integrating convolution and self-attention mechanisms, further enhances the extraction of spatio-temporal features. We refer to this module as the CFACM (Channel Fusion & Attention Convolution Mix) module. Our approach not only outperforms the latest efficient spatio-temporal action detection models in terms of lightness, reducing the model size by 24% compared to the latter, but also improves the mAP accuracy on the UCF101-24 dataset by 1.35%, while maintaining excellent speed performance, thus achieving a balance between accuracy and speed. Furthermore, existing models often use 3D convolutions to extract temporal information, which may be limited on certain devices, such as Apple’s M series processors. To mitigate the potential issue of 3D convolution operators not being supported during edge deployment of spatio-temporal action detection models, we employ a spatio-temporal shift module containing only 2D convolutions. This enables the model to acquire temporal information and inject the obtained temporal features into multi-level spatio-temporal feature extraction models. This not only liberates the model from the constraints of 3D convolution operations but also enhances the model’s balance between accuracy and speed. This results in state-of-the-art performance in lightweight networks using only 2D convolutions.

Compared with traditional methods, the action recognition model based on 3D convolutional deep neural network captures spatio-temporal features more accurately, resulting in higher accuracy. However, the large number of parameters and computational requirements of 3D models make it difficult to deploy on mobile devices with limited computing power. In order to achieve an efficient video action recognition model, we have analyzed and compared classic lightweight network principles and proposed the 3D-ShuffleViT network. By deeply integrating the self-attention mechanism with convolution, we have introduced an efficient ACISA module that further enhances the performance of our proposed model. This has resulted in exceptional performance in both context-sensitive and context-independent action recognition, while reducing deployment costs. It is worth noting that our 3D-ShuffleViT network, with a computational cost of only 6% of that of SlowFast-ResNet101, achieved 98% of the latter’s Top1 accuracy on the EgoGesture dataset. Furthermore, on the same CPU (Intel i5-8300H), its speed was 2.5 times that of the latter. In addition, when we deployed our model on edge devices, our proposed network achieved the best balance between accuracy and speed among lightweight networks of the same order.

Belief rule base (BRB) has attracted considerable interest due to its interpretability and exceptional modeling accuracy. Generally, BRB construction relies on prior knowledge or historical data. The limitations of knowledge constrain the knowledge-based BRB and are unsuitable for use in large-scale rule bases. Data-driven techniques excel at extracting model parameters from data, thus significantly improving the accuracy of BRB. However, the previous data-based BRBs neglected the study of interpretability, and some still depend on prior knowledge or introduce additional parameters. All these factors make the BRB highly problem-specific and limit its broad applicability. To address these problems, a model-agnostic statistical BRB (MAS-BRB) modeling approach is proposed in this paper. It adopts a model-agnostic statistical methodology for parameter extraction, ensuring that the parameters both fulfill their intended roles within the BRB framework and accurately represent complex, nonlinear data relationships. A comprehensive interpretability analysis of MAS-BRB components further confirms their compliance with established BRB interpretability standards. Experiments conducted on multiple public datasets demonstrate that MAS-BRB not only achieves improved modeling performance but also shows greater effectiveness compared to existing rule-based and traditional machine learning models.