针对动态场景下视觉SLAM(Simultaneous Localization and Mapping)系统中深度学习分割网络实时性不足,以及相机非期望运动导致位姿估计偏差的问题,提出一种基于跨域掩膜分割的视觉SLAM算法.该算法采用轻量化YOLO-fastest网络结合背景减...针对动态场景下视觉SLAM(Simultaneous Localization and Mapping)系统中深度学习分割网络实时性不足,以及相机非期望运动导致位姿估计偏差的问题,提出一种基于跨域掩膜分割的视觉SLAM算法.该算法采用轻量化YOLO-fastest网络结合背景减除法实现运动物体检测,利用深度图结合深度阈值分割构建跨域掩膜分割机制,并设计相机运动几何校正策略补偿检测框坐标误差,在实现运动物体分割的同时提升处理速度.为优化特征点利用率,采用金字塔光流对动态特征点进行帧间连续跟踪与更新,同时确保仅由静态特征点参与位姿估计过程.在TUM数据集上进行系统性评估,实验结果表明,相比于ORB-SLAM3算法,该算法的绝对位姿误差平均降幅达97.1%,与使用深度学习分割网络的DynaSLAM和DS-SLAM的动态SLAM算法相比,其单帧跟踪时间大幅减少,在精度与效率之间实现了更好的平衡.展开更多
[ Objective] This study was to breed rice cultivars with multi-resistance to Orseolia oryzae （Wood-Mason）. [ Method] The Guangxi local cultivar GX-M001 （Jiangchao） with high resistance to Orseolia oryzae （Wood-Ma...[ Objective] This study was to breed rice cultivars with multi-resistance to Orseolia oryzae （Wood-Mason）. [ Method] The Guangxi local cultivar GX-M001 （Jiangchao） with high resistance to Orseolia oryzae （Wood-Mason） was used to hybrid with the known resistance cultivars ＂Kangwenqingzhan＂ （harboring GM5 gene）, OB677（ harboring GM3 gene） from Sri Lanka, HT1350 and high yield end quality cultivar ＂ Guiruanzhan＂. [ Result] Through pyramiding the multi-resistant genes via routine hybridization, the general resistances of the hybrids were remarkably enhanced. The grades of resistance were also improved, many of the combinations were endowed with a resistance at immune level （grade 0） ; and interestingly, the respective hybridization of GX-M001 （high resistance） with OB677（ medium resistance） and HT1350（suscepti- ble） also generate two lines at immune level, which is probably the effects of additive effects of genes.[ Conclusion] By routine hybridization, multiple genes were successfully pyramided, thus generating novel rice lines with multiple resistances. For the rice breeding scientists at the grass-roots level, the resistance-resistance pyramiding is an effective approach to breed high resistance cultivars.展开更多
Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and comple...Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.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
Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet ...Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.展开更多
针对现有深度学习技术在建筑工地高空作业人员安全带检测任务中,存在施工环境复杂、距离远导致检测效率低下的问题,提出一种融合边缘特征增强的级联群注意力工地施工安全带检测模型。首先,该模型在主干网络中进行边缘特征增强,有效提升...针对现有深度学习技术在建筑工地高空作业人员安全带检测任务中,存在施工环境复杂、距离远导致检测效率低下的问题,提出一种融合边缘特征增强的级联群注意力工地施工安全带检测模型。首先,该模型在主干网络中进行边缘特征增强,有效提升了网络对复杂场景下模糊目标形态结构和边缘细节的特征提取;其次,设计CSP(cross stage partial)模块,通过融合级联群注意力(cascaded group attention,CGA)机制,减少冗余信息,提高计算效率;最后,引入融合ELA(efficient local attention)注意力的特征金字塔ELA-HSFPN,实现高、中、低不同尺度的跨分辨率特征高效的动态加权融合,提升了检测精度。实验结果表明,改进后的算法与基准模型YOLOv11n相比,其平均精度值mAP提升了8.27百分点,检测效果明显得到提升,并且和其他的主流目标检测算法相比,也有明显改善,验证了新模型的有效性。展开更多
文摘针对动态场景下视觉SLAM(Simultaneous Localization and Mapping)系统中深度学习分割网络实时性不足,以及相机非期望运动导致位姿估计偏差的问题,提出一种基于跨域掩膜分割的视觉SLAM算法.该算法采用轻量化YOLO-fastest网络结合背景减除法实现运动物体检测,利用深度图结合深度阈值分割构建跨域掩膜分割机制,并设计相机运动几何校正策略补偿检测框坐标误差,在实现运动物体分割的同时提升处理速度.为优化特征点利用率,采用金字塔光流对动态特征点进行帧间连续跟踪与更新,同时确保仅由静态特征点参与位姿估计过程.在TUM数据集上进行系统性评估,实验结果表明,相比于ORB-SLAM3算法,该算法的绝对位姿误差平均降幅达97.1%,与使用深度学习分割网络的DynaSLAM和DS-SLAM的动态SLAM算法相比,其单帧跟踪时间大幅减少,在精度与效率之间实现了更好的平衡.
基金Supported by National Natural Science Foundation of China(30760117)National Key Technology R &D Program (2007BAD68B01)~~
文摘[ Objective] This study was to breed rice cultivars with multi-resistance to Orseolia oryzae （Wood-Mason）. [ Method] The Guangxi local cultivar GX-M001 （Jiangchao） with high resistance to Orseolia oryzae （Wood-Mason） was used to hybrid with the known resistance cultivars ＂Kangwenqingzhan＂ （harboring GM5 gene）, OB677（ harboring GM3 gene） from Sri Lanka, HT1350 and high yield end quality cultivar ＂ Guiruanzhan＂. [ Result] Through pyramiding the multi-resistant genes via routine hybridization, the general resistances of the hybrids were remarkably enhanced. The grades of resistance were also improved, many of the combinations were endowed with a resistance at immune level （grade 0） ; and interestingly, the respective hybridization of GX-M001 （high resistance） with OB677（ medium resistance） and HT1350（suscepti- ble） also generate two lines at immune level, which is probably the effects of additive effects of genes.[ Conclusion] By routine hybridization, multiple genes were successfully pyramided, thus generating novel rice lines with multiple resistances. For the rice breeding scientists at the grass-roots level, the resistance-resistance pyramiding is an effective approach to breed high resistance cultivars.
基金supported by the National Natural Science Foundation of China(No.62203163)the Scientific Research Project of Hunan Provincial Education Department(No.24A0519)+1 种基金the Hunan Provincial Natural Science Foundation(No.2025JJ60407)the Postgraduate Scientific Research Innovation Project of Hunan Province(No.CX2024100).
文摘Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring.However,conventional detection approaches are highly susceptible to noise,illumination variations,and complex environmental conditions,which often reduce detection accuracy and real-time performance.To address these limitations,we propose Lightweight and Precise YOLO(LP-YOLO),a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid,built upon YOLOv8.First,to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks(CNNs),we design an enhanced backbone based on Wavelet Convolutions(WTConv),which expands the receptive field through multifrequency convolutional processing.Second,a Bidirectional Feature Pyramid Network(BiFPN)is employed to achieve bidirectional feature fusion,enhancing the representation of smoke features across scales.Third,to mitigate the challenge of ambiguous object boundaries,we introduce the Frequency-aware Feature Fusion(FreqFusion)module,in which the Adaptive Low-Pass Filter(ALPF)reduces intra-class inconsistencies,the offset generator refines boundary localization,and the Adaptive High-Pass Filter(AHPF)recovers high-frequency details lost during down-sampling.Experimental evaluations demonstrate that LP-YOLO significantly outperforms the baseline YOLOv8,achieving an improvement of 9.3%in mAP@50 and 9.2%in F1-score.Moreover,the model is 56.6%and 32.4%smaller than YOLOv7-tiny and EfficientDet,respectively,while maintaining real-time inference speed at 238 frames per second(FPS).Validation on multiple benchmark datasets,including D-Fire,FIRESENSE,and BoWFire,further confirms its robustness and generalization ability,with detection accuracy consistently exceeding 82%.These results highlight the potential of LP-YOLO as a practical solution with high accuracy,robustness,and real-time performance for smoke and fire source detection.
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.
基金partially supported by the Natural Science Foundation of Shandong Province,China(No.ZR2023ME009)the National Natural Science Foundation of China(No.51909252)。
文摘Accurately estimating depth from underwater monocular images is essential for the target tracking task of unmanned underwater vehicles.This work proposes a method based on the Lpg-Lap Unet architecture.First,the Unet architecture integrates Laplacian pyramid depth residuals and Sobel operators to improve the boundary details in depth images,which may suffer from the feature loss caused by upsampling and the blurriness of underwater images.Multiscale local planar guidance layers then fully exploit the intermediate depth features,and a comprehensive loss function ensures robustness and accuracy.Experimental results on benchmarks demonstrate the effectiveness of Lpg-Lap Unet and its superior performance over state-of-the-art models.An underwater target tracking system is then designed to further validate its real-time capabilities in the AirSim simulation platform.
文摘针对现有深度学习技术在建筑工地高空作业人员安全带检测任务中,存在施工环境复杂、距离远导致检测效率低下的问题,提出一种融合边缘特征增强的级联群注意力工地施工安全带检测模型。首先,该模型在主干网络中进行边缘特征增强,有效提升了网络对复杂场景下模糊目标形态结构和边缘细节的特征提取;其次,设计CSP(cross stage partial)模块,通过融合级联群注意力(cascaded group attention,CGA)机制,减少冗余信息,提高计算效率;最后,引入融合ELA(efficient local attention)注意力的特征金字塔ELA-HSFPN,实现高、中、低不同尺度的跨分辨率特征高效的动态加权融合,提升了检测精度。实验结果表明,改进后的算法与基准模型YOLOv11n相比,其平均精度值mAP提升了8.27百分点,检测效果明显得到提升,并且和其他的主流目标检测算法相比,也有明显改善,验证了新模型的有效性。
