一种改进的雾天图像行人和车辆检测算法  

Algorithm for foggy-image pedestrian and vehicle detection

作　　者：汪昱东 郭继昌[1] 王天保 WANG Yudong;GUO Jichang;WANG Tianbao(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津大学电器自动化与信息工程学院,天津300072

出　　处：《西安电子科技大学学报》2020年第4期70-77,共8页Journal of Xidian University

基　　金：国家自然科学基金(61771334)。

摘　　要：由于雾天图像数据集不足、雾天表现形式多样等因素,使得基于深度学习的目标检测网络在雾天图像行人和车辆检测中容易出现过拟合,造成鲁棒性不佳和准确率不高等问题。针对上述问题,在检测网络中加入雾浓度判别模块以提高网络的适应性和鲁棒性,通过引入可变形卷积和注意力机制以提升卷积神经网络的特征提取能力,通过模拟合成雾天图像的方式扩充数据集以加快网络的收敛速度。实验结果表明,改进后的网络针对雾天图像行人和车辆检测,其检测平均准确率相较于基于候选框的检测网络有约2%~4%的提高,且未显著地增加网络的训练参数和计算量。In order to improve the accuracy of the foggy-image pedestrian and vehicle detection,a novel and practical Foggy-image pedestrian and vehicle detection network(FPVDNet)based on the Faster R-CNN is proposed.First,a foggy-density discriminating module(FDM)is proposed to influence the density of the foggy images.In this way,the prediction from the FDM could determine the subsequent operations for different densities of the fog(No-fog,Light fog,and Dense fog).Then,the squeeze and excitation module(SE Module)is designed to use the attention mechanism to improve the feature extraction capability of the network.Meanwhile,the method of the deformable convolution network is applied to add offsets and learn the offsets from target tasks to enhance the transformation modeling capacity of CNNs.Finally,for lack of the annotated fog image dataset,it is necessary to generate a simulated fog image training dataset through the atmospheric scattering model.The simulated foggy image inherits the annotation of the clear image and increases the information on the fog density.Experiments by the proposed FPVDNet are carried out on the 1,500 real-fog images and 500 real-clear images,with experimental results showing that,compared with the original Faster R-CNN,the mean average detection accuracies are improved 2%~4%by using the FPVDNet.

关 键 词：雾天图像目标检测 深度学习 基于候选框的检测网络 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]