Article
Version 1
Preserved in Portico This version is not peer-reviewed
Looking Deeper into Images for Autonomous Road Weather Detection
Version 1
: Received: 9 December 2022 / Approved: 13 December 2022 / Online: 13 December 2022 (02:30:49 CET)
A peer-reviewed article of this Preprint also exists.
Samo, M.; Mafeni Mase, J.M.; Figueredo, G. Deep Learning with Attention Mechanisms for Road Weather Detection. Sensors 2023, 23, 798. Samo, M.; Mafeni Mase, J.M.; Figueredo, G. Deep Learning with Attention Mechanisms for Road Weather Detection. Sensors 2023, 23, 798.
Abstract
There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.
Keywords
Computer vision; Deep learning; Image classification; Loss functions; Vision Transformers; Weather detection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
