室内低/弱纹理、光照不足的场景下,视觉惯导融合的即时定位与建图(simultaneous localization and mapping,SLAM)定位精度明显优于纯视觉SLAM方法。然而,当前基于点特征的视觉惯导SLAM方法通常难以检测并追踪足够的特征,同时惯性测量单...室内低/弱纹理、光照不足的场景下,视觉惯导融合的即时定位与建图(simultaneous localization and mapping,SLAM)定位精度明显优于纯视觉SLAM方法。然而,当前基于点特征的视觉惯导SLAM方法通常难以检测并追踪足够的特征,同时惯性测量单元的先验测量信息亦未充分利用,导致SLAM整体定位精度低、鲁棒性弱。针对这些问题,构建一种自适应点线特征和惯性测量单元(inertial measurement unit,IMU)耦合的视觉SLAM方法。首先设计一种自适应的快速角点特征检测算法,以增强图像特征点检测的鲁棒性。另外,快速线特征检测算法易检测短线、断线,且图像因光照变化易导致线特征的“过提取”或“错提取”。因此,利用边缘检测二值图像构造自适应线特征提取算法,并借助消影点的特性筛选聚类线特征。然后,由点线特征重投影误差和IMU先验预积分位姿估计量,通过松耦合为SLAM前端位姿估计和算法提供稳健的初始化结果。随后,后端利用紧耦合建立视觉和IMU观测量的统一非线性最小化残差函数,并优化得到准确的图像帧位姿。最后,在开源数据集上测试验证,并对比几种经典SLAM方法。实验结果表明,所构建的SLAM方法平均定位精度至少提高12%,同时具有较强的鲁棒性。展开更多
Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
