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Fast learning method for convolutional neural networks using extreme learning machine and its application to lane

Jihun Kim¹, Jonghong Kim¹, Gil-Jin Jang¹

¹School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society

|January 23, 2017

Summary

This summary is machine-generated.

This study introduces a novel deep learning approach for enhanced driving lane detection using convolutional neural networks (CNNs) combined with extreme learning machines (ELMs). The method significantly reduces training time and improves accuracy for autonomous driving systems.

Keywords:

Advanced driver assistance system Convolutional neural network Extreme learning machine Lane detection

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Deep learning, particularly convolutional neural networks (CNNs), excels in image analysis tasks like object detection.
Traditional CNN training demands substantial computational resources and large datasets.
Driving lane detection typically involves edge and line detection, often requiring image preprocessing.

Purpose of the Study:

To develop an efficient deep learning model for enhancing images and detecting driving lanes on motorways.
To address the computational and data requirements of conventional CNNs for lane detection.

Main Methods:

Utilized a CNN for image enhancement to remove noise and irrelevant obstacles before lane detection.
Proposed a novel learning algorithm for CNNs by integrating an extreme learning machine (ELM).
Implemented a stacked ELM architecture within the CNN framework and modified backpropagation for effective weight learning.

Main Results:

The proposed method significantly reduced training time compared to conventional CNNs.
Achieved accurate driving lane detection with minimal training data.
Demonstrated improved performance in image enhancement and lane detection tasks.

Conclusions:

The integration of ELM with CNNs offers an efficient and effective solution for driving lane detection.
The modified backpropagation algorithm aids in maintaining performance while learning network weights.
This approach holds promise for real-time applications in autonomous driving systems.