18337099. METHOD FOR GENERATING MATRIX DATA FOR CONVOLUTIONAL NEURAL NETWORK AND LEARNING SYSTEM USING CONVOLUTIONAL NEURAL NETWORK simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

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METHOD FOR GENERATING MATRIX DATA FOR CONVOLUTIONAL NEURAL NETWORK AND LEARNING SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

Kazuyuki Sasaki of Nisshin-shi (JP)

METHOD FOR GENERATING MATRIX DATA FOR CONVOLUTIONAL NEURAL NETWORK AND LEARNING SYSTEM USING CONVOLUTIONAL NEURAL NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 18337099 titled 'METHOD FOR GENERATING MATRIX DATA FOR CONVOLUTIONAL NEURAL NETWORK AND LEARNING SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

Simplified Explanation

The patent application describes a method for generating matrix data for a convolutional neural network that performs a convolution operation based on the matrix data, specifically focusing on vehicle information arranged as matrix elements.

  • The matrix data is composed of predetermined time-series data where each row changes continuously in terms of time in the arrangement direction of each column.
  • The time-series data consists of first data with a high degree of influence on the convolution operation and second data with a lower degree of influence.
  • The convolution operation is carried out using a kernel that partitions the matrix data into rows and columns corresponding to a predetermined coefficient.
  • At least one row of the first data is arranged for each set of rows corresponding to the coefficient.
      1. Potential Applications

- Autonomous driving systems - Traffic monitoring and analysis - Vehicle tracking and identification

      1. Problems Solved

- Efficient processing of vehicle information in convolutional neural networks - Improved accuracy in vehicle detection and classification - Handling time-series data with varying degrees of influence in convolution operations

      1. Benefits

- Enhanced performance of convolutional neural networks in analyzing vehicle data - Better understanding and utilization of time-series data in machine learning algorithms - Potential for more accurate and reliable vehicle-related applications


Original Abstract Submitted

In a method for generating matrix data for a convolutional neural network that performs a convolution operation based on the matrix data in which vehicle information is arranged as an matrix element, the matrix data is composed of predetermined time-series data in which each row changes continuously in terms of time in an arrangement direction of each column, the time-series data is composed of first data of which degree of influence on the convolution operation is high and second data of which degree of influence is lower than the first data, the convolution operation is performed using a kernel that partitions the matrix data into the rows and columns corresponding to a predetermined coefficient, and at least one row of the first data is arranged for each set of rows corresponding to the coefficient.

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