车载红外和可见光图像配准方法  

Automotive Infrared and Visible Light Image Registration Method

作　　者：苏建彬 沈英[2] 黄磊 沈元兴 SU Jianbin;SHEN Ying;HUANG Lei;SHEN Yuanxing(Fuzhou Polytechnic,Fuzhou 350108,China;Fuzhou University,Fuzhou 350108,China;Second Military Representative Office of the Army Equipment Department in the Nanjing area,Nanjing 210000,China)

机构地区：[1]福州职业技术学院,福建福州350108 [2]福州大学,福建福州350108 [3]陆军装备部驻南京地区第二军事代表室,江苏南京210000

出　　处：《红外技术》2024年第10期1209-1217,共9页Infrared Technology

基　　金：国家自然科学基金(62005049)。

摘　　要：为了提高车辆视觉感知能力,针对交通场景运用提出一种改进的轮廓角方向(contour angle orientation,CAO)算法用于实现红外与可见光图像配准。通过模拟不同的交通场景,对成熟算法进行性能检测对比,选出CAO算法这一优势算法,并对其粗匹配参数和图像预处理图像缩放程序做了改进。实验表明,改进后的CAO算法细匹配更精准,马赛克拼接图拼接处衔接更加自然,线条更加顺滑,效果更好。与原来CAO算法相比,改进后的算法均方根误差值RMSE下降3.29%,查准率Precision提高2.13%,平均运算耗时减少0.11s,在配准精度和配准实时性方面均证明了算法的改进效果。To enhance the visual perception of vehicles,an improved contour angle orientation(CAO)algorithm is proposed for the registration of infrared and visible light images in traffic scenes.By simulating different traffic scenarios,a performance comparison was conducted among mature algorithms to select the superior CAO algorithm.Subsequently,improvements were made to the coarse matching parameters and image preprocessing scaling procedure.Experiments demonstrate that the refined CAO algorithm achieves more precise fine matching,thus resulting in mosaic stitching with smoother transitions and lines and yielding better results.Compared with the original CAO algorithm,the improved version reduces the RMSE value by 3.29%,increases the precision value by 2.13%,and decreases the average computation time by 0.11 s,thereby demonstrating improvements in both registration accuracy and real-time performance.

关 键 词：红外和可见光图像 图像配准 特征提取 轮廓角方向 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]