Toward the recognition of spacecraft feature components:A new benchmark and a new model

Countries are increasingly interested in spacecraft surveillance and recognition which play an important role in on-orbit maintenance,space docking,and other applications.Traditional detection methods,including radar,have many restrictions,such as excessive costs and energy supply problems.For many on-orbit servicing spacecraft,image recognition is a simple but relatively accurate method for obtaining sufficient position and direction information to offer services.However,to the best of our knowledge,few practical machine-learning models focusing on the recognition of spacecraft feature components have been reported.In addition,it is difficult to find substantial on-orbit images with which to train or evaluate such a model.In this study,we first created a new dataset containing numerous artificial images of on-orbit spacecraft with labeled components.Our base images were derived from 3D Max and STK software.These images include many types of satellites and satellite postures.Considering real-world illumination conditions and imperfect camera observations,we developed a degradation algorithm that enabled us to produce thousands of artificial images of spacecraft.The feature components of the spacecraft in all images were labeled manually.We discovered that direct utilization of the DeepLab V3+model leads to poor edge recognition.Poorly defined edges provide imprecise position or direction information and degrade the performance of on-orbit services.Thus,the edge information of the target was taken as a supervisory guide,and was used to develop the proposed Edge Auxiliary Supervision DeepLab Network(EASDN).The main idea of EASDN is to provide a new edge auxiliary loss by calculating the L2 loss between the predicted edge masks and ground-truth edge masks during training.Our extensive experiments demonstrate that our network can perform well both on our benchmark and on real on-orbit spacecraft images from the Internet.Furthermore,the device usage and processing time meet the demands of engineering applications.

Research on will-dimension SIFT algorithms for multi-attitude face recognition

The results of face recognition are often inaccurate due to factors such as illumination,noise intensity,and affine/projection transformation.In response to these problems,the scale invariant feature transformation(SIFT) is proposed,but its computational complexity and complication seriously affect the efficiency of the algorithm.In order to solve this problem,SIFT algorithm is proposed based on principal component analysis(PCA) dimensionality reduction.The algorithm first uses PCA algorithm,which has the function of screening feature points,to filter the feature points extracted in advance by the SIFT algorithm;then the high-dimensional data is projected into the low-dimensional space to remove the redundant feature points,thereby changing the way of generating feature descriptors and finally achieving the effect of dimensionality reduction.In this paper,through experiments on the public ORL face database,the dimension of SIFT is reduced to 20 dimensions,which improves the efficiency of face extraction;the comparison of several experimental results is completed and analyzed to verify the superiority of the improved algorithm.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.