Fully convolutional neural network for Malaysian road lane detection

• Main Authors: Zakaria, N. J., Zamzuri, H., Ariff, M. H., Shapiai, M. I., Saruchi, S. A., Hassan, N.
• Format: Article
• Language: English
• Published: Science Publishing Corporation Inc. 2018
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/id/eprint/85137/1/NJZakaria2018_FullyConvolutionalNeuralNetworkforMalaysian.pdf; http://eprints.utm.my/id/eprint/85137/; http://dx.doi.org/10.14419/ijet.v7i4.11.20792

Recently, a deep learning, Fully Convolutional Neural Network (FCN) has been widely studied because it can demonstrate promising results in the application of detection of objects in an image or video. Hence, the FCN approach has been proposed as one of the solution methods in mitigating the issues pertinent to Malaysia’s road lane detection. Previously, FCN model for lane detection has not been tested in Malaysian road conditions. Therefore, this study investigates the further performance of this model in the Malaysia. The network model is trained and validated using the datasets obtained from Machine Learning NanoDegree. In addition, the real-time data collection has been conducted to collect the data sets for the testing at the highway and urban areas in Malaysia. Then, the collected data is used to test the performance of the FCN network in detecting the lane markings on Malaysia road. The results demonstrated that the FCN method is achieving 99% of the training and validation accuracy.