Spectral clustering algorithm has proved be more effective than most traditional algorithms in finding clusters. However, its high computational complexity limits its effect in actual application. This paper combines the spectral clustering with MapReduce, through evaluation of sparse matrix eigenvalue and computation of distributed cluster, puts forward the improvement ideas and concrete realization, and thus improves the clustering speed of the distinctive clustering algorithm. According to the experiment, with the processing data scale being enlarged, the clustering rate is in nearly linear growth, and the proposed parallel spectral clustering algorithm is suitable for large data mining. The research results provide research basis to better design a clustering partition algorithm in large data and high efficiency.

Non-intrusive load identification can infer the operating status of various electrical appliances in a user’s household based on the electrical parameters collected by the overall household electricity meter. It helps users utilize electrical energy more scientifically and rationally. Due to significant differences in the working principles of appliances and their energy consumption, the effectiveness of load identification has not yet met practical application requirements. This paper proposes a load identification method based on a dual-branch attention GRU fusion network structure, the network integrates GRU, Temporal Convolution Network (TCN), and attention mechanisms. It overcomes the limitation of GRU networks in extracting ultra-long-term temporal dependencies and compensates for TCN’s less effective exploration of medium and short-term temporal dependencies compared to GRU. As a result, it achieves better identification performance. Experiments on public datasets indicate that the proposed method achieves better load state identification performance compared to other methods.

Traffic sign detection, as a critical branch of object detection, plays an essential role in both assisted driving and autonomous driving technologies. In this paper, we propose MASG-Net, a lightweight detection network designed to improve the accuracy and efficiency of traffic sign detection. First, we introduce a channel attention mechanism into MobileNetV3 to create a novel E-block structure and design E-mobilenet, a lightweight backbone network, to replace the backbone in YOLOv4-tiny, significantly enhancing feature extraction while reducing parameters. Second, we propose a multi-scale dilated convolution spatial pyramid pooling (MDSPP) module to expand the receptive field of feature maps, enabling the network to capture multi-scale contextual information effectively. Finally, a semantic information guidance (SIG) module is introduced to leverage deep semantic information to guide shallow feature layers, improving the detection of small traffic signs and enhancing robustness against cluttered backgrounds. Experimental results on the CCTSDB, GTSDB and TT100K datasets demonstrate that MASG-Net achieves superior detection performance, particularly for small and challenging traffic signs, while maintaining high efficiency with an inference speed of 203.6 FPS. These results highlight MASG-Net’s potential for real-time traffic sign detection in practical applications.

Image segmentation is an important research in image processing and machine vision in which automated driving can be seen the main application scene of image segmentation algorithms. Due to the many constraints of power supply and communication in in-vehicle systems, the vast majority of current image segmentation algorithms are implemented based on the deep learning model. Despite the ultrahigh segmentation accuracy, the problem of mesh artifacts and segmentation being too severe is obvious, and the high cost, computational, and power consumption devices required are difficult to apply in real-world scenarios. It is the focus of this paper to construct a road scene segmentation model with simple structure and no need of large computing power under the premise of certain accuracy. In this paper, the ESPNet (Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) model is introduced in detail. On this basis, an improved ESPNet model is proposed based on ESPNet. Firstly, the network structure of the ESPNet model is optimized, and then, the model is optimized by using a small amount of weakly labeled and unlabeled scene sample data. Finally, the new model is applied to video image segmentation based on dash cam. It is verified on Cityscape, PASCAL VOC 2012, and other datasets that the algorithm proposed in this paper is faster, and the amount of parameters required is less than 1% of other algorithms, so it is suitable for mobile terminals.