摘要
针对无人机图像中违章建筑多为小目标且存在部分遮挡目标导致的检测速率慢、误检率高的问题,提出一种基于YOLOv5网络的违章建筑检测方法。在原来的批量标准化模块开始和结束处分别添加中心和缩放校准增强有效特征并形成更稳定的特征分布,加强网络模型的特征提取能力。用平滑处理后的KL(Kullback-Leibler)散度损失函数替换原损失函数置信度中的交叉熵,进一步提高模型的泛化性能。对YOLOv5的主干特征提取网络进行改进,将残差模块替换为LSandGlass模块减少信息损失并剔除低分辨率的特征层以减少语义丢失。实验结果表明,与原版的YOLOv5相比,改进后模型的训练更容易使得网络收敛,检测违章建筑的速度有了较大提升,同时提高了检测的精确度。
Aiming at solving the problem of slow detection rate and high false detection rate caused by the illegal buildings in the UAV images,which are mostly small targets and partially occluded targets,a detection method of illegal buildings based on YOLOv5 network is proposed.Firstly,at the beginning and end of original batch normalization module,centering and scaling calibration are added to enhance the effective features and form a more stable feature distribution,which strengthens the feature extraction ability of network model.Then,the smoothed KL(Kullback-Leibler)divergence loss function is adopted to replace the cross entropy in the confidence of original loss function,which further improves the generalization performance of model.Finally,the backbone feature extraction network of YOLOv5 is improved,and the residual module is replaced with the LSandGlass module to reduce information loss and eliminate low-resolution feature layers to reduce semantic loss.Experimental results show that the training of the proposed improved model is easier to make network converge in comparison with original YOLOv5,and the speed of detecting illegal buildings has been greatly improved,and then detection accuracy has been improved.
作者
于娟
罗舜
YU Juan;LUO Shun(School of Economics and Management,Fuzhou University,Fuzhou 350108,China)
出处
《计算机工程与应用》
CSCD
北大核心
2021年第20期236-244,共9页
Computer Engineering and Applications
基金
国家自然科学基金(71771054)。
