Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high qualit...Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.展开更多
复杂交通环境下目标检测中存在很多外界干扰因素,导致通用的目标检测算法效果较差。针对目标检测方法中全局特征信息利用不充分,小目标、遮挡目标检测精度低,以及模型计算量大等问题,提出一种基于改进YOLOv5s的融合全局特征目标检测方...复杂交通环境下目标检测中存在很多外界干扰因素,导致通用的目标检测算法效果较差。针对目标检测方法中全局特征信息利用不充分,小目标、遮挡目标检测精度低,以及模型计算量大等问题,提出一种基于改进YOLOv5s的融合全局特征目标检测方法。首先,对YOLOv5s的主干网络进行扩展,得到更深层的特征图以增强较大目标的语义信息;其次,在此基础上引入全局信息融合模块代替原模型中的Neck部分,以3D卷积的方式融合各尺度信息;然后,设计了一种基于位置的先验框匹配方法,在原图尺度上搜索与真实框匹配的先验框;最后,使用Copy-Paste数据增强方法增大小目标样本数量并使用DIoU�NMS作为后处理方法进行非极大值抑制。该模型在BDD100K数据集中平均精确率(mean Average Preci⁃sion,mAP)为54.55%,检测速度为63.72帧每秒(Frames Per Second,FPS)。与原始YOLOv5s算法相比,该方法在检测速度及精度方面均有明显优势。展开更多
基金Supported by the National Natural Science Foundation of China(No.61308099,61304032)
文摘Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.
文摘复杂交通环境下目标检测中存在很多外界干扰因素,导致通用的目标检测算法效果较差。针对目标检测方法中全局特征信息利用不充分,小目标、遮挡目标检测精度低,以及模型计算量大等问题,提出一种基于改进YOLOv5s的融合全局特征目标检测方法。首先,对YOLOv5s的主干网络进行扩展,得到更深层的特征图以增强较大目标的语义信息;其次,在此基础上引入全局信息融合模块代替原模型中的Neck部分,以3D卷积的方式融合各尺度信息;然后,设计了一种基于位置的先验框匹配方法,在原图尺度上搜索与真实框匹配的先验框;最后,使用Copy-Paste数据增强方法增大小目标样本数量并使用DIoU�NMS作为后处理方法进行非极大值抑制。该模型在BDD100K数据集中平均精确率(mean Average Preci⁃sion,mAP)为54.55%,检测速度为63.72帧每秒(Frames Per Second,FPS)。与原始YOLOv5s算法相比,该方法在检测速度及精度方面均有明显优势。
