@article{ref1,
title="Deep and transfer learning approaches for pedestrian identification and classification in autonomous vehicles",
journal="Electronics (Basel, Switzerland)",
year="2021",
author="Mounsey, Alex and Khan, Asiya and Sharma, Sanjay",
volume="10",
number="24",
pages="e3159-e3159",
abstract="Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model.<p /> <p>Language: en</p>",
language="en",
issn="2079-9292",
doi="10.3390/electronics10243159",
url="http://dx.doi.org/10.3390/electronics10243159"
}