Yong Li

Semantic segmentation of large-scale power-corridor LiDAR point clouds is essential for remote sensing-based transmission line inspection, vegetation encroachment monitoring, and intelligent grid maintenance. However, existing methods still struggle with massive data volumes, severe class imbalance, sparse power-related objects, and high computational cost in power corridor scenes. To address these challenges, this article proposes GSSP-KAN, an efficient semantic segmentation network that integrates Kansformer with grouped separable sparse convolution. The Kansformer module enhances nonlinear feature representation and contextual modeling, while the Grouped Separable Sparse Convolution Block (GSSP_Block) reduces redundant self-attention computation and preserves fine-grained local geometric structures. GSSP-KAN is evaluated on four large-scale datasets, including NW-3D, NeiMeng-3D, Nanning, and Toronto-3D. Experimental results show that GSSP-KAN achieves 98.20% OA/88.50% mIoU on NW-3D, 99.96%/98.58% on NeiMeng-3D, 98.20%/96.70% on Nanning, and 97.90%/83.80% on Toronto-3D. Compared with the baseline, the proposed model reduces the parameter count to 14.7 M and accelerates inference by 24.0% on NW-3D and 30.8% on Toronto-3D.

Efficient cleaning of floating waste using an intelligent unmanned surface vehicle is an important development trend in inland water governance. Path planning is the core of the decision-making module for unmanned surface vehicle waste cleaning and is key to achieving autonomous operation of the unmanned surface vehicle. However, due to the complexity and dynamic changes of the water surface environment, unmanned surface vehicle path planning methods for floating waste face challenges such as small size, uncertainty, and uneven distribution of floating waste. In response to the above issues, this article studies the problem of insufficient integration and low efficiency between existing path planning algorithms and target-perception modules, and designs an efficient overall path planning method for floating-waste cleaning by an unmanned surface vehicle. This method transforms the path planning problem of floating-waste cleaning unmanned surface vehicle into a Traveling Salesman Problem by setting global patrol points and tracking local targets, and proposes an improved ant colony algorithm, IACO, to solve the Traveling Salesman Problem. This article is based on the TSPLIB dataset and practical applications for experiments. The experimental results show that the proposed method has average optimal path lengths of 75.930 m, 446.555 m, and 703.759 m on the Ulysses22, eil51, and st70 datasets, respectively, which are reduced by 0.355 m, 4.108 m, and 13.575 m compared to the benchmark.

To meet the requirement of 3D object detection task , an efficient point clouds correlation enhancement RCNN(EPCE-RCNN) is proposed. The proposed method reduces the computational complexity and time consumption of the network through a lightweight proposal generation module, and accelerates the generation of the 3D proposal box. Meanwhile, during region of interest feature coding, the relevance among different grid points is enhanced through an efficient self-attention pooling module, so that the limitation that the pooling operation is influenced by the radius of a neighborhood query sphere is addressed. In addition, the combination of an attention mechanism and a feedforward network ensures the nonlinearity of the model, so that the model can perform feature expression better. Thus, the synchronous improvement of the network detection efficiency and the detection precision is realized. On the KITTI dataset, the detection accuracy of three difficulty levels reaches 89.99%, 81.69% and 77.17% respectively. Compared with the baseline Voxel-RCNN, the detection efficiency of EPCE-RCNN is improved by 12%. To verify the generalization and application value of the proposed method, a power equipment dataset with 3D label information is constructed, the 3D label frame information of the YCB dataset is also supplemented. Experiments are carried out on these datasets. In the experimental results of the verification set, the mAP of a mug, gelatin box, single clip, wedge clip and C clip can reach 37.67%, 40.06%, 35.63%, 30.01% and 37.31% respectively. Compared with the baseline, the proposed algorithm has a significant improvement and its generalization has been fully verified.

As an essential power device, insulators are present in large numbers across various transmission lines. Insulators are continuously affected by the natural environment, leading to fractures, pollution accumulation, and other issues. These problems can cause flashovers and pollution flash discharge accidents, trigger relay protection devices, and result in significant economic losses. However, it is challenging for existing algorithms to achieve a good balance between accuracy and speed in many insulator datasets. Therefore, in this paper, we construct a novel feature extraction module named the point-depth module (PD module) to reduce feature loss and computational complexity. We also design the ShortPath-FPN (SP-FPN), which introduces the Attentional Feature Fusion module (AFF) to effectively shorten the information flow from input to head and enhance feature maps. Compared to state-of-the-art (SOTA) algorithms, the PD module significantly reduces the amount of computation with only a slight impact on accuracy, while the SP-FPN module effectively improves both accuracy and detection speed. We enhance the YOLOV5m model based on the above modules to achieve an accuracy of 94.1% and an FPS of 77 for insulator detection, meeting the requirements for real-time detection while improving detection accuracy and outperforming other methods.