坑洼是一种常见的路面病害,会降低行车安全,准确快速地检测路面坑洼较为重要。针对现有坑洼检测方法在小目标和密集目标的场景下检测精度不高的问题,文中提出了一种改进YOLOv5(You Only Look Once version 5)模型。在YOLOv5的主干网络...坑洼是一种常见的路面病害,会降低行车安全,准确快速地检测路面坑洼较为重要。针对现有坑洼检测方法在小目标和密集目标的场景下检测精度不高的问题,文中提出了一种改进YOLOv5(You Only Look Once version 5)模型。在YOLOv5的主干网络中引入CBAM(Convolutional Block Attention Module)来提高模型对关键特征的注意能力,将YOLOv5的损失函数改为EIoU(Efficient Intersection over Union)来提高模型对目标的检测精度。实验结果表明,所提模型能够在小目标和密集目标的场景下快速准确地检测路面坑洼,在开源数据集Annotated Potholes Image Dataset中的mAP(mean Average Precision)达到了82%,较较于YOLOv5和其他主流方法也有所提高。展开更多
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movemen...Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.展开更多
文摘坑洼是一种常见的路面病害,会降低行车安全,准确快速地检测路面坑洼较为重要。针对现有坑洼检测方法在小目标和密集目标的场景下检测精度不高的问题,文中提出了一种改进YOLOv5(You Only Look Once version 5)模型。在YOLOv5的主干网络中引入CBAM(Convolutional Block Attention Module)来提高模型对关键特征的注意能力,将YOLOv5的损失函数改为EIoU(Efficient Intersection over Union)来提高模型对目标的检测精度。实验结果表明,所提模型能够在小目标和密集目标的场景下快速准确地检测路面坑洼,在开源数据集Annotated Potholes Image Dataset中的mAP(mean Average Precision)达到了82%,较较于YOLOv5和其他主流方法也有所提高。
基金supported by the Zhejiang Key Laboratory of General Aviation Operation Technology(No.JDGA2020-7)the National Natural Science Foundation of China(No.62173237)+3 种基金the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Talent Project of Revitalization Liaoning Province(No.XLYC1907022)the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the High-Level Innovation Talent Project of Shenyang(No.RC190030).
文摘Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.
