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

• Published in: Neural networks Vol. 87; pp. 109 - 121
• Main Authors: Kim, Jihun, Kim, Jonghong, Jang, Gil-Jin, Lee, Minho
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2017
• Subjects: Advanced driver assistance system; Algorithms; Artificial Intelligence; Convolutional neural network; Extreme learning machine; Humans; Image Enhancement - methods; Lane detection; Machine Learning; Neural Networks (Computer); Advanced driver assistance system; Lane detection; Extreme learning machine; Convolutional neural network
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2016.12.002

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance.