关键词： Deep learning  

摘要： This study aims to enhance the extraction capabilities of point cloud features in LiDAR object detection and improve the accuracy of object detection. This study proposes a multi-stage feature extraction method target detection for object detection based on PointPillars. First, the maximum pooling in the point cloud feature encoding target detection module is improved by introducing combined pooling to minimize spatial information loss during the pooling process. Additionally, a global attention mechanism is introduced to enhance the ability of the pseudo-image feature to express information. Finally, a cavity convolution module is integrated into the column feature extraction network to increase the receptive field and further improve the feature extraction capability. Experimental results on the KITTI data set show that the average detection accuracy of the proposed method improves by 2. 91 percentage points compared with the original network. Moreover, the inference speed reaches 17. 08 frame/s, which meets the requirements for real-time detection in autonomous driving. © 2025 Universitat zu Koln. All rights reserved.

关键词： Optical remote sensing  

摘要： To address the issues of missed and false detection in high-resolution optical remote sensing images caused by complex backgrounds, large variations in target sizes, and a high proportion of small targets, an improved YOLOv8s object detection algorithm is proposed. First, the weighted bi-directional feature pyramid network (BiFPN) structure is introduced to replace the original neck network, and an additional branch from the backbone network is added to capture shallow features, thereby enhancing the performance of small-target detection. Second, the efficient multi-scale attention (EMA) mechanism is incorporated into the Bottleneck of the neck’s cross-stage partial network fusion (C2f) module to improve the focus on spatial and channel information while reducing interference from irrelevant information. Finally, based on SCYLLA-IoU (SIoU), SWIoU loss function is designed by combining the dynamic non-monotonic focus coefficient from wise-IoU (WIoU). This strengthens the performance of bounding box regression and considers the directional matching between the predicted and ground truth boxes, further improving the detection accuracy of the algorithm. On the DOTA dataset, the improved algorithm achieves a mean average precision (mAP50) of 73. 0%, which is a 3. 0 percentage points increase compared to that of YOLOv8s, and its detection results also outperforms other mainstream algorithms. Furthermore, the experimental results obtained on the RSOD and NWPU NHR-10 datasets further validate the generality and adaptability of the proposed algorithm across different datasets. © 2025 Universitat zu Koln. All rights reserved.

关键词： 数字图像处理   工程测量   实践应用  

摘要： 现代信息技术以及通信技术的迅猛发展,在前沿科学技术融入不同领域的基础条件下催生了数字图像处理方法,其在工程测量领域扮演的角色无可取代。数字图像处理技术具有精确度较高、分辨率高、非接触远程测量、形象信息丰富等多方面的特征,以其独特的优越性在我国的工程测量领域中得到了广泛的应用。本篇文章主要分析了数字图像处理在工程测量中的关键应用技术,并且就数字图像处理技术在工程测量中的实践应用进行了探究,希望能够为推动数字图像处理技术在工程测量领域中进一步发挥应用价值提供参考意见。

关键词： 水下视觉   目标跟踪   场景感知   图卷积网络   双视图图对比学习  

摘要： 水下视觉目标跟踪是自主水下航行器(AUV)场景理解的核心技术之一。然而, 复杂水下环境中的光照不均、背景干扰和目标外观变化等问题, 严重影响传统视觉目标跟踪方法的准确性和稳定性。现有方法主要依赖目标的表观建模, 难以实现复杂环境下可靠跟踪, 尤其是在相似目标干扰的情况下, 容易导致误识别和目标漂移。文中提出了一种基于场景感知的水下单目标跟踪方法, 通过基于区域分割的图卷积模块提取场景内所有目标区域, 并结合图卷积网络建模目标区域与周围关键区域的长距离依赖关系, 显著提升对相似目标的区分能力。此外, 文中引入双视图图对比学习策略, 通过生成随机扰动的目标特征视图, 实现图卷积模块的无监督在线更新, 使得模型能够在复杂环境下保持较强的适应性和稳定性。实验表明, 所提方法在跟踪精度和鲁棒性方面显著优于经典方法, 尤其在光照变化大、背景复杂和相似目标干扰较强的场景下, 成功率和精确度均有明显提升。这表明文中研究有效解决了水下目标跟踪中因光照变化和背景干扰导致的目标漂移问题, 能在相似目标存在时保持稳定跟踪, 为水下无人系统提供了高效可靠的目标跟踪解决方案。

关键词： 数字图像处理   人工智能背景   教学内容   教学方法   教学改革  

摘要： 随着人工智能技术蓬勃发展,数字图像处理技术迭代速度加快,因为该课程有应用广泛和多领域交叉融合的特性,传统教学模式需要革新,在这样的背景下,数字图像处理技术课程教学改革变得很迫切,要把前沿技术和知识内容紧密结合,推动该领域教育现代化,本文从多个维度探讨课程内容优化、教学方法创新、教学考核体系构建、明确教学目标、转变教学模式以及更新教学理念等关键问题,打破传统框架限制,培育有卓越专业素养的数字图像处理技术人才。

关键词： 人工智能技术   智慧广电   计算机视觉   自然语言处理  

摘要： 人工智能技术的快速发展,为智慧广电建设注入了新的活力。本文围绕人工智能技术的特点,结合计算机视觉、自然语言处理、视频分析等关键技术,探讨了在智慧广电建设领域的应用模式。重点探索制播流程、网络维护、运营管理、用户服务等环节的技术融合,研究证明人工智能对内容生产、资源管理及服务优化的推动作用。通过本文探索显示,人工智能技术可有效提升广电行业内容生产效率,优化用户体验。

关键词： Vehicle detection  

摘要： In multi-target tracking and detection tasks, it is necessary to continuously track multiple targets, such as vehicles, pedestrians, etc. To achieve this goal, the system must be able to continuously acquire and process image frames containing these targets. These consecutive frame images enable the algorithm to update the position and state of the target in real-time in each frame of the image. How to accurately associate the detected target with the target in the previous or next frame to form a stable trajectory is a complex problem. Therefore, a multi object tracking and detection method for intelligent driving vehicles based on YOLOv5 and point cloud 3D projection is proposed. Using Retinex algorithm to enhance the image of the environment in front of the vehicle, remove light interference in the image, and build an intelligent detection model based on YOLOv5 network structure. The enhanced image is input into the model, and multiple targets in front of the vehicle are identified through feature extraction and target localization. By combining point cloud 3D projection technology, the correlation between the position changes of adjacent frame images in the projection coordinate system can be inferred. By sequentially projecting the multi-target recognition results of multiple consecutive frame images into the 3D laser point cloud environment, effective tracking of the motion trajectories of all targets in front of the vehicle can be achieved. The experimental results show that the application of this method for intelligent driving vehicle front multi-target tracking and detection yields a MOTA (Tracking Accuracy) value greater than 30, demonstrating its superior tracking and detection performance. Copyright © 2025, The Authors. All rights reserved.