Research On Segmentation And Recognition Algorithm For High-speed Railway

The intrusion object detection technology based on video intelligent analysis has become a vital development direction in the field of high-speed railway perimeter security detection,one of the problems that currently restricts the wide application of video analysis technology is the automatic identification of alarm areas.To achieve a fast,accurate,and automatic segmentation and recognition,a fast image segmentation algorithm based on adaptive feature gradient detector and a fast image recognition algorithm for the local area based on simplified convolutional neural network are proposed,and an effective balance has been reached in the areas of calculation speed,segmentation precision,recognition accuracy,parameter calculation amount and manual participation.Firstly,aiming at the large calculation caused by the existing segmentation algorithm,the strong linear characteristics of the railway scene is analyzed and a fast segmentation algorithm based on multi-feature fusion and the optimization of adaptive feature gradient detector is proposed.This algorithm first uses the Hough transform to identify the maximum value of the linear feature of the railway scene to adjust the feature gradient detector for extracting the gradient distribution of the image.Then,a small amount of optimized detectors are applied to simultaneously calculate the pixel color&texture feature distribution,and the pixel similarity distribution to obtain an accurate boundary point with weight,further preventing fragmented regions in image from being excessively fragmented by automatically screening strong and weak boundary points;finally,a combination rule is designed to quickly combine the fragmented regions into local areas based on the boundary point weight and the size of the fragmented regions.The comparison experiment results show that the proposed algorithm based on feature-fusion and adaptive gradient detector can extract the precise boundary of local areas quickly and effectively,which lays an optimal foundation for the subsequent region recognition.Aiming at the problem that the existing local area identification by convolutional neural network is computationally intensive,and occupies a large amount of memory,and requires real-time processing by the GPU,a pre-training-based convolution kernel weight optimization algorithm is proposed in this paper.By constructing a shallow autoencoder network,the algorithm learns the basic feature templates of different regions of the railway automatically,the pre-trained kernels will solve the model optimizing problem caused by the various feature in the same category and the bad image quality of the same object when the illumination and camera factors changed.The comparative experimental results show that the proposed algorithm has effectively improves the recognition accuracy of the traditional convolutional neural network in the partial region of the railway scene,and it also provides solutions for similar application problems.To further accelerate the computational speed of railway scene segmentation and recognition,this paper proposes a two-step region segmentation algorithm.In the first phase,the ultra-lightweight convolutional neural network is used to quickly scan the railway scene and determine the potential location of the track area.In the second phase,the potential location is accurately segmented and identified.Targeting at the problem of low accuracy of ultra-light quantitative convolutional network,a sparse cost function of convolution channel is proposed to optimize the training process and enhance the difference of feature map.The comparative experimental results show that the two-stage algorithm greatly reduces the redundant computing time of the panoramic image,and the accuracy rate of the ultra-lightweight convolution network is effectively compensated,which greatly improves the system's working efficiency and performance.Compared with the existing technology,the series of algorithms proposed in this paper greatly improve the pixel-level accuracy of railway scene segmentation and recognition,the calculation time is comparatively shorter,the network parameters are less,the reliance on GPU graphics card is eliminated,the application system cost is reduced,and easy to migrate to different configurations of data processing platform.Currently,this algorithm is being put to practical use on the Shanghai-Nanjing Intercity High-speed Railway...