电动车骑行者的安全问题已成为社会焦点,而佩戴安全头盔被证明是减少事故伤害的有效方法。为加强道路交通安全,提高监管效率,本文提出一种基于智能通信和深度学习的无人机辅助头盔智能检测算法。通过结合智能通信技术,无人机可以实时传...电动车骑行者的安全问题已成为社会焦点,而佩戴安全头盔被证明是减少事故伤害的有效方法。为加强道路交通安全,提高监管效率,本文提出一种基于智能通信和深度学习的无人机辅助头盔智能检测算法。通过结合智能通信技术,无人机可以实时传输视频数据并通过智能算法进行快速分析。本文首先提出改进的Outlook-C2f架构,以提高算法对小目标的关注度;其次,在特征金字塔网络(FPN)中使用CARAFE代替上采样,动态生成权重,以实现精确的特征重构,提高空间分辨率;最后,集成WIoU以提高定位信息的准确性。实验结果表明,基于道路实拍数据集,改进后的YOLOv8算法的mAP(mean average precision)和FPS(frames per second)分别达到96.7%和26.91帧/s,显著优于主流算法,展现了其在复杂交通场景中的应用潜力。展开更多
YOLO(You Only Look Once)是一种流行的目标检测模型,该模型利用单一神经网络架构即可对图像中的目标进行位置和类别的预测,极大地提高了检测的效率。将YOLO模型应用于植物虫害的检测,不仅可以加快识别速度,还可以提升识别精度。文章阐...YOLO(You Only Look Once)是一种流行的目标检测模型,该模型利用单一神经网络架构即可对图像中的目标进行位置和类别的预测,极大地提高了检测的效率。将YOLO模型应用于植物虫害的检测,不仅可以加快识别速度,还可以提升识别精度。文章阐述了YOLO模型目前在植物虫害检测领域的应用现状,介绍了该模型的优势与面临的挑战,并对其在未来发展进程中的改进策略与方向进行了展望。展开更多
驾驶员在实际驾驶的过程中会存在面部遮挡场景,例如戴眼镜、戴口罩等,传统单一通过提取驾驶员面部特征进行疲劳检测的Dlib算法不再适用。该文结合Dlib与YOLO11使用多阈值判定,对传统Dlib疲劳检测算法进行改进,给出戴眼镜、戴口罩等驾驶...驾驶员在实际驾驶的过程中会存在面部遮挡场景,例如戴眼镜、戴口罩等,传统单一通过提取驾驶员面部特征进行疲劳检测的Dlib算法不再适用。该文结合Dlib与YOLO11使用多阈值判定,对传统Dlib疲劳检测算法进行改进,给出戴眼镜、戴口罩等驾驶员面部遮挡场景的疲劳检测算法,并在Raspberry Pi 5硬件平台,使用公开数据集验证改进算法对于驾驶员疲劳检测的准确性。另外,改进算法还可以对吸烟、打电话等这类分心驾驶行为进行检测和语音提醒,对疲劳和分心行为实现更全面的检测和预警。展开更多
In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes ...In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络...针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络,检测潜在动态区域,并结合Lucas-Kanade(LK)光流法识别其中的动态特征点,从而在剔除动态特征点的同时保留静态特征点,提高特征点利用率和位姿估计精度。此外,新增语义地图构建线程,通过去除YOLO11n识别到的动态物体点云,并融合前端提取的语义信息,实现静态语义地图的构建。在TUM数据集上的实验结果表明,相较于ORB-SLAM3,该算法在高动态序列数据集中的定位精度提升了95.02%,验证了该算法在动态环境下的有效性,能显著提升视觉SLAM系统的定位精度和地图构建质量。展开更多
文摘电动车骑行者的安全问题已成为社会焦点,而佩戴安全头盔被证明是减少事故伤害的有效方法。为加强道路交通安全,提高监管效率,本文提出一种基于智能通信和深度学习的无人机辅助头盔智能检测算法。通过结合智能通信技术,无人机可以实时传输视频数据并通过智能算法进行快速分析。本文首先提出改进的Outlook-C2f架构,以提高算法对小目标的关注度;其次,在特征金字塔网络(FPN)中使用CARAFE代替上采样,动态生成权重,以实现精确的特征重构,提高空间分辨率;最后,集成WIoU以提高定位信息的准确性。实验结果表明,基于道路实拍数据集,改进后的YOLOv8算法的mAP(mean average precision)和FPS(frames per second)分别达到96.7%和26.91帧/s,显著优于主流算法,展现了其在复杂交通场景中的应用潜力。
文摘YOLO(You Only Look Once)是一种流行的目标检测模型,该模型利用单一神经网络架构即可对图像中的目标进行位置和类别的预测,极大地提高了检测的效率。将YOLO模型应用于植物虫害的检测,不仅可以加快识别速度,还可以提升识别精度。文章阐述了YOLO模型目前在植物虫害检测领域的应用现状,介绍了该模型的优势与面临的挑战,并对其在未来发展进程中的改进策略与方向进行了展望。
文摘驾驶员在实际驾驶的过程中会存在面部遮挡场景,例如戴眼镜、戴口罩等,传统单一通过提取驾驶员面部特征进行疲劳检测的Dlib算法不再适用。该文结合Dlib与YOLO11使用多阈值判定,对传统Dlib疲劳检测算法进行改进,给出戴眼镜、戴口罩等驾驶员面部遮挡场景的疲劳检测算法,并在Raspberry Pi 5硬件平台,使用公开数据集验证改进算法对于驾驶员疲劳检测的准确性。另外,改进算法还可以对吸烟、打电话等这类分心驾驶行为进行检测和语音提醒,对疲劳和分心行为实现更全面的检测和预警。
基金funded by the Jiangxi SASAC Science and Technology Innovation Special Project and the Key Technology Research and Application Promotion of Highway Overload Digital Solution.
文摘In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
文摘针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络,检测潜在动态区域,并结合Lucas-Kanade(LK)光流法识别其中的动态特征点,从而在剔除动态特征点的同时保留静态特征点,提高特征点利用率和位姿估计精度。此外,新增语义地图构建线程,通过去除YOLO11n识别到的动态物体点云,并融合前端提取的语义信息,实现静态语义地图的构建。在TUM数据集上的实验结果表明,相较于ORB-SLAM3,该算法在高动态序列数据集中的定位精度提升了95.02%,验证了该算法在动态环境下的有效性,能显著提升视觉SLAM系统的定位精度和地图构建质量。
