改进AOD-Net的道路交通图像去雾算法  

Improved AOD-Net Algorithm for Dehazing Road Traffic Images

作　　者：孟修建 乔欢欢 王雅 程晓 MENG Xiu-Jian;QIAO Huan-Huan;WANG Ya;CHENG Xiao(Shaanxi Motorway Mechanization Engineering Co.Ltd.,Xi’an 710038,China;College of Information Engineering,Chang’an University,Xi’an 710064,China)

机构地区：[1]陕西高速机械化工程有限公司,西安710038 [2]长安大学信息工程学院,西安710064

出　　处：《计算机系统应用》2024年第1期206-212,共7页Computer Systems & Applications

基　　金：国家重点研发计划(2019YFB1600100)。

摘　　要：针对现有图像去雾算法在处理道路交通图像时无法同时兼顾去雾效果和实时性的问题,本文以快速一体化网络去雾算法(AOD-Net)为基础进行改进.首先,在AOD-Net中添加SE通道注意力,以自适应的方式分配通道权重,关注重要特征;其次,引入金字塔池化模块,扩大网络的感受野,并融合不同尺度特征,更好地捕捉图像信息;最后,使用复合损失函数同时关注图像像素信息和结构纹理信息.实验结果表明,改进后的AOD-Net算法对道路交通图像去雾后的峰值信噪比提升了2.52 dB,结构相似度达到了91.2%,算法复杂度和去雾耗时略微增加,但仍满足实时要求.In order to address the problem that existing image dehazing algorithms cannot simultaneously consider both dehazing effects and real-time performance when processing road traffic images,a fast all-in-one dehazing network(AOD-Net)algorithm is improved in this study.Firstly,SE channel attention is added to the AOD-Net to adaptively allocate channel weights and focus on important features.Secondly,a pyramid pooling module is introduced to enlarge the receptive field of the network and fuse the features in different scales,so as to better capture image information.Finally,a composite loss function is used to simultaneously focus on image pixel information and structural texture information.Experimental results show that the improved AOD-Net algorithm increases the peak signal-to-noise ratio(SNR)of road traffic images by 2.52 dB after dehazing,and the structural similarity reaches 91.2%.The algorithm complexity and dehazing time are slightly increased,but still meet real-time requirements.

关 键 词：图像去雾 深度学习 AOD-Net算法 通道注意力 金字塔池化 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U495[自动化与计算机技术—计算机科学与技术]