Abstract
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.
Highlights
An intelligent vehicle with the implementation of an intelligent system such as an Advanced Driver Assistant System (ADAS) is designed to assist the driver in improving the driving process and vehicle collision
The training and validation datasets were obtained from the Machine Learning Nanodegree [10]
This paper presents a deep learning approach namely Fully Convolutional Neural Network (FCN) for Malaysia road lane detection
Summary
An intelligent vehicle with the implementation of an intelligent system such as an Advanced Driver Assistant System (ADAS) is designed to assist the driver in improving the driving process and vehicle collision. This study used a frontfacing camera to perform the lane detection as it can capture the whole front facing scene, including all possible, clutters This step provides more information about the road scene images for the network to learn. This paper proposes a Fully Convolutional Neural Networks (FCN) method in detecting the lane markings on Malaysian roads. It took the arbitrary size of the input road lane images and detected the pixel-wise version of this images by using a semantic segmentation process. The main contribution of this paper is to propose the extraction of the full-scale features for semantic segmentation using the existing FCN model and to further investigate the performance of the model by testing the network under Malaysian road conditions. The conclusions and the future works are drawn in the last section
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