基于局部对比度和全卷积网络的小空间碎片检测  

Small Space Debris Detection Based on Local Contrast Measure and Fully Convolutional Network

作　　者：陶江 曹云峰 TAO Jiang;CAO Yunfeng(College of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学航天学院,南京211106

出　　处：《计算机测量与控制》2023年第7期15-20,27,共7页Computer Measurement &Control

基　　金：中央高校基本科研业务费(NP2019105);南京航空航天大学博士学位论文创新与创优基金(BCXJ19-11)。

摘　　要：近年来环绕地球轨道的空间碎片数量急剧增加,对在轨运行的航天器构成严重威胁;基于天基监视平台的空间碎片小目标快速检测对于航天器远距离规避具有重要意义,然而由于空间碎片小目标的快速相对运动和宇宙射线产生的噪声,空间碎片小目标快速检测极具挑战;提出了一种基于深度卷积神经网络的天基空间碎片小目标显著性检测方法,首先,使用局部对比度方法获得输入图像的空间对比度图;然后,通过全卷积网络结合上述对比度图捕获时空显著性信息;最后,基于仿真视频序列图像开展实验验证,可实现对最远距离为30 km,最小为16像素的目标准确检测;通过设置不同的高斯白噪声方差参数模拟空间环境噪声,证明了在噪声背景条件下对空间碎片小目标检测的有效性和鲁棒性。The number of space debris has increased dramatically,it has a serious threat to spacecraft operating in orbit.Rapid small space debris detection with a space-based surveillance platform is very important for spacecraft emergency avoidance at distance.Nevertheless,small space debris detection is a great challenge due to its fast movement and noise caused by cosmic rays in the space-based surveillance platform.Aimed at above problem,a rapid small space-based debris saliency detection method based on a deep convolutional neural network is proposed.Firstly,the spatial contrast map of the input image is obtained by using the local contrast method.Then,the spatiotemporal saliency information is captured by incorporating with the above contrast map.Finally,the experiments are conducted based on the synthetic video sequence images.It can detect the space debris with the furthest distance of 30 km and the smallest size of 16 pixels.Through simulating the cosmic space environmental noise with different Gaussian white noise variances,the experimental results show that the proposed method has the applicability and robustness of the small debris detection under the noise background.

关 键 词：空间碎片 小目标 局部对比度 显著性检测 卷积神经网络 

分 类 号：V249.3[航空宇航科学与技术—飞行器设计] TP391[自动化与计算机技术—计算机应用技术]