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关键词： Binocular vision  

摘要： The three-dimensional perception of road objects in challenging environments is crucial for the development of autonomous vehicles that operating in all conditions，at all hours. Infrared binocular vision mimics the human binocular system，facilitating stereoscopic perception of objects in challenging conditions such as dim or zero-light environments. The core technology for stereoscopic perception in binocular vision systems is accurate object detection and matching. To streamline the complex sequence of object detection and matching procedures，a synchronous object detection and matching network（SODMNet）is proposed，which can perform synchronous detection and matching of infrared objects. SODMNet innovatively combines an object detection network with an object matching module，leveraging the deep features from the classification and regression branches as inputs for the object matching module. By concatenating these features with relative position encoding from the feature maps and processing the concatenated features through a convolutional network，the network generates feature descriptors for the left and right images. Object matching is then achieved by calculating the Euclidean distances between these descriptors，thus facilitating synchronous object detection and matching in binocular vision. In addition，a novel nighttime infrared binocular dataset，annotated with targets such as pedestrians and vehicles，is created to support the development and evaluation of the proposed network. Experimental results indicate that SODMNet achieves a significant improvement of more than 84. 9% in object detection mean average precision（mAP）on this dataset，with an object matching average precision（AP）of 0. 5777. These results demonstrate that SODMNet is capable of high-precision，synchronized object detection and matching in infrared binocular vision，marking a significant advancement in the field. © 2025 Chinese Optical Society. All rights reserved.

关键词： Graph neural networks  

摘要： Human action recognition based on skeletal graph neural network has attracted much attention by virtue of its simplicity and robustness, and graph data have a natural advantage in processing human skeletal information, which has gradually become a research hotspot in the field of action recognition. Starting from the broad basic concept of action recognition, this paper further introduces the task of human action recognition using skeletal graph neural networks, and summarizes the research results on human action recognition using skeletal graph neural networks in recent years from four aspects. The paper introduces different methods for constructing topological graphs with graph structures, the common mechanisms used in action recognition models, the comparison of commonly used datasets and evaluation indicators with mainstream methods. Finally, the problems of human action recognition based on skeletal graph neural networks are elaborated in detail with respect to the current state of research, and an outlook on the future development of the field is given based on the current state of research. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： convolutional neural network   forest fire recognition   smoke recognition   target detection  

摘要： [Objective]Since both flame and smoke are dynamic targets,it makes forest fire small targets and smoke detection in aerial view challenging. The construction of forest fire small target and smoke detection model based on convolutional neural network has positive significance in balancing the number of parameters and detection accuracy of forest fire small target and smoke target.[Method]For the real-time requirements,the high real-time based FasterNet is improved and the backbone network layer of FRSCnet was constructed. The neck network layer of FRSCnet was constructed using group shuffle convolution (GSConv). In the head network,the convolutional block attention module (CBAM)was introduced. Finally,receptive field block (RFB)feature extraction module was added to the first layer of the neck network. [Result]FasterNet employs a unique partial convolution (PConv)technique that effectively reduce redundant computations and memory accesses,thus ensuring the high-speed detection performance of the model when processing aerial *** is able to balance the number of parameters of the model with the performance of the feature fusion,thus reducing the complexity of the model while ensuring the detection accuracy. GSConv is able to balance the number of parameters and feature fusion performance of the model,thus reducing the complexity of the model while ensuring detection accuracy. With the CBAM attention mechanism,the model is able to better focus on forest fire small targets and smoke targets to improve the detection performance while increasing the number of parameters as little as possible. To increase the receptive field of the model to better extract forest fire small targets and smoke targets in aerial *** (receptive field block)feature extraction module,which can increase the receptive field of the model to better extract forest fire small targets and smoke targets in aerial view. The experimental results show that the proposed FRSCnet model achieves a

关键词： 网络亚文化   青年   价值观培育  

摘要： 文章聚焦于网络亚文化给青年正确价值观培育带来的负面影响，探寻网络亚文化对青年产生负面影响的成因，并在此基础上提出解决途径，即化“离散”为凝聚，充实主流文化体系；化“反叛”为认同，增强社会集体归属感；化“麻痹”为进取，营造健康的舆论环境。

关键词： “Z世代”大学生   自我同一性理论   网络表达   正向引导  

摘要： “Z世代”的“网生化”特点使得他们有着独特的生活方式和价值理念，同时使得他们容易受到网络价值取向的直接冲击，自我同一性理论则为理解“Z世代”大学生当下的网络表达行为提供了系统的理论框架。文章借由自我同一性理论的相关观点，分析“Z世代”大学生网络表达的特点，探讨其网络表达失范的内在逻辑，并提出对“Z世代”大学生网络表达正向引导的策略，即主动引导，聚焦“Z世代”大学生特点；精准对接，关注网络表达背后的情感诉求；培育引领，优化思政教育范式。

关键词： 独立董事网络   非效率投资   企业风险承担   双创板块   高质量发展  

摘要： 基于社会网络理论和代理理论,本文以2011～2022年我国创业板、科创板上市公司为样本,实证检验独立董事网络对非效率投资的影响机制.研究结果表明,独立董事网络中心度与独立董事网络结构洞均能显著抑制企业非效率投资,并且独立董事网络主要通过提升企业的风险承担水平来抑制非效率投资.进一步研究发现,融资约束会削弱独立董事网络对企业非效率投资的抑制作用.异质性分析发现,在东部地区企业、非国有企业中独立董事网络对非效率投资的抑制作用更显著.

关键词： 劳动力培训   供应链网络   网络均衡   变分不等式   数字化转型  

摘要： 人口老龄化和疫情等因素导致劳动力不足及结构性失衡，威胁着企业供应链安全。构建了一个考虑劳动力投资成本和数字技术水平的供应链网络优化模型，并采用变分不等式方法求解。通过算例分析，考察了培训成本和数字技术对企业利润、劳动力需求、产品需求量及价格的影响。研究发现，提高数字技术水平和增加劳动力培训能够促进需求增长、降低价格、减少劳动力并提升企业利润。随着技术水平的提升，所需培训成本逐步增加，劳动力需求逐步下降。在竞争环境中，技术水平较高的企业通过培训更容易提高利润，技术水平较低的企业需先提升技术水平，再进行劳动力培训，才能增强市场竞争力。