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Visual inspection method of intelligent logistics warehouse guide line based on deep learning

A deep learning and visual detection technology, applied in neural learning methods, logistics, biological neural network models, etc., to achieve the effect of simple network training operation, less requirements for image preprocessing, and good generalization performance

Active Publication Date: 2018-11-16
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

[0007] Traditional machine vision algorithms require manual design of targeted features. Manually designed feature selection is usually only effective in specific occasions. After the scene changes, it is necessary to re-extract features and adjust model parameters, which has great limitations in practical applications. sex

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  • Visual inspection method of intelligent logistics warehouse guide line based on deep learning
  • Visual inspection method of intelligent logistics warehouse guide line based on deep learning
  • Visual inspection method of intelligent logistics warehouse guide line based on deep learning

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Embodiment Construction

[0024] The accompanying drawings are for illustrative purposes only, and should not be construed as limitations on this patent; in order to better illustrate this embodiment, certain components in the accompanying drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art It is understandable that some well-known structures and descriptions thereof may be omitted in the drawings. The positional relationship described in the drawings is for illustrative purposes only, and should not be construed as a limitation on this patent.

[0025] The logistics warehouse guide line visual detection method proposed by the present invention is based on the deep learning model of the Fully Convolutional Neural Network (FCN), and proposes a model construction training and testing process through the Pytorch and Caffe2 frameworks, and can The implementation scheme of deploying the model on embedded development platforms such ...

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Abstract

The invention relates to the technical field of detection methods and more particularly to a visual inspection method of an intelligent logistics warehouse guide line based on deep learning. The method comprises a training phase and a testing phase. The training phase includes the steps of training data acquisition and marking, model construction, model training, model verification and comparison,model selection and model conversion. The test phase includes data input, guide line detection and detection result fitting. The method proposed by the invention has high flexibility, the size and depth of a convolution kernel of a neural network used for detection can be modified according to different performance requirements, and different precision requirements and running time requirements are satisfied.

Description

technical field [0001] The present invention relates to the technical field of detection methods, and more specifically, to a method for visual detection of guiding lines in intelligent logistics warehouses based on deep learning. Background technique [0002] Accurate positioning and navigation are the key to AGV's automatic transportation tasks. At present, AGVs, which are widely used for sorting and transportation tasks in warehouses, mainly use electromagnetic rails for route control. In the later stage of warehouse construction, magnetic wires need to be laid on the warehouse floor, which increases the construction cost of smart warehouses. It is even more difficult to rebuild the old warehouses that have been built and are in operation by laying magnetic wires. In order to simplify the construction and reconstruction of logistics warehouses and reduce costs, many new navigation methods, such as guide line navigation, two-dimensional code navigation, synchronous positi...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G06Q10/08
CPCG06N3/08G06Q10/08G06V20/56G06N3/045G06F18/23
Inventor 成慧申静怡周佺
Owner SUN YAT SEN UNIV
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