Restoration And Research Of Motion Blur Image In The Process Of Automatic Docking Of Boarding Bridge

With the development of smart airports,boarding bridges are also developing in a fully automatic direction.In the automatic docking process based on computer vision,the motion blur generated by relative motion is one of the biggest influencing factors.In this paper,in order to solve the cabin door motion blur image generated during the boarding bridge docking process,the cabin door motion blur image The main contents of the research are as follows:(1)Based on the Deblur GAN network model framework,a motion blur image restoration algorithm for cabin doors based on a generative confrontation network is designed.First,the residual network is improved,and two residual network improvement strategies are proposed.Add to the generator model,one is Deblur GAN-Sandglass,which analyzes the influence of the residual network by discussing the order of the excitation layer and 1*1 convolution.The other is Deblur GAN-Res Next,which removes the batch normalization layer in the model.At the same time,the loss function WGAN in the discriminator is optimized,and the WGAN-up optimization scheme is proposed.(2)Based on the advantages of encoder-decoder structure and single-scale channel,a multi-stage progressive door motion blur image restoration algorithm based on convolutional neural network is designed.The framework is divided into three stages.The first two stages merge the encoder-decoder network,and the final stage uses the original input resolution sub-network.A supervised attention module is added between each stage,and a cross-stage feature fusion mechanism is also introduced.On this basis,two optimization schemes are proposed for the attention model-series mixed attention model and parallel mixed attention model.(3)First of all,the traditional algorithm is used to simulate and analyze the cabin door image,and it is found that the restoration effect is not good in the case of noise.Secondly,using the method of deep learning,combined experiment analysis of the improvement schemes to get the best scheme,the experiment comparison found that the improvement algorithm based on the Deblur GAN model has improved the restoration index to a certain extent.Compared with other algorithms,the CNN-based multi-stage restoration algorithm has better restoration indicators.Through experimental analysis,the performance difference of the improved scheme in the series mixing sequence,pooling effect,and series-parallel connection is proved,which proves the effectiveness of the improved method.In order to prove the versatility of the algorithm,in addition to the hatch data set,training and testing are also carried out on the GOPRO data set.The results show that they have good restoration effects,which can produce good restoration effects on the blurred images of the cabin door motion in the actual docking process,and meet the requirements of automatic docking of the boarding bridge.