Method and system for classifying vehicle-borne laser-point cloud targets

A vehicle-mounted laser and target classification technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as fast and effective recognition and classification, and achieve the effect of improving the degree of automation and strong robustness

Active Publication Date: 2017-05-10
FUZHOU UNIV
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Problems solved by technology

[0004] To this end, it is necessary to provide a vehicle-mounted laser point cloud target classification method and system to solve the problem of the inability to quickly classify different grou

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  • Method and system for classifying vehicle-borne laser-point cloud targets
  • Method and system for classifying vehicle-borne laser-point cloud targets
  • Method and system for classifying vehicle-borne laser-point cloud targets

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Embodiment Construction

[0042] In order to explain in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and accompanying drawings.

[0043] First, explain some English abbreviations in this implementation mode:

[0044] RBM (Restricted Boltzmann Machine): Restricted Boltzmann machine, a probabilistic generative model consisting of a visible layer and a hidden layer. The entire network is a fully connected inter-layer, no-connected dichotomy within the layer. Structural undirected graph. ,

[0045] DBN (Deep Belief Network): Deep Belief Network is a generative model composed of multiple RBM stacks. By training the weights between its neurons, we can let the entire neural network generate training data according to the maximum probability.

[0046] DBSCAN (Density-Based Spatial Clustering of Applications with Noise): A noise-based density-based clustering method is...

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Abstract

The invention relates to the technical field of deep learning, and in particular to a method and a system for classifying vehicle-borne laser-point cloud targets. The method for classifying the vehicle-borne laser-point cloud targets comprises the following steps of preprocessing vehicle-borne laser-point cloud data to generate a to-be-classified target point cloud; constructing a basic training sample database; generating an input characteristic vector; constructing a deep belief network; training the deep belief network; generating the characteristic vector of the to-be-classified target point cloud, and completing classification of the vehicle-borne laser-point cloud targets by taking the characteristic vector as an input characteristic of the trained deep belief network. Automatic identification and classification of the vehicle-borne laser-point cloud data are realized by use of the deep belief network, the automation degree of identification and classification of the target point cloud is effectively improved, and the method and the system are strong in robustness and can be applied to vehicle-borne laser-point cloud data with complicated scenes.

Description

technical field [0001] The invention relates to the field of deep learning technology, in particular to a method and system for classifying vehicle-mounted laser point cloud objects. Background technique [0002] Vehicle-Borne Laser Scanning System (Vehicle-Borne Laser Scanning System), as an emerging surveying and mapping technology this year, can quickly and accurately obtain three-dimensional spatial information of roads and features on both sides of the road, and has become one of the important means for rapid acquisition of urban street spatial data , widely used in basic surveying and mapping, urban planning and design, intelligent transportation and other fields. Compared with the rapid development of vehicle-mounted laser scanning system hardware, the identification and classification technology of vehicle-mounted laser scanning data target features is relatively lagging behind. Improving the efficiency and intelligence of vehicle-mounted laser scanning data classifi...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/084G06F18/23G06F18/24G06F18/214
Inventor 方莉娜罗海峰陈崇成
Owner FUZHOU UNIV
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