Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Surjeet Dalal, Bijeta Seth, Magdalena Radulescu (), Teodor Florin Cilan and Luminita Serbanescu
Additional contact information
Surjeet Dalal: Department of Computer Science and Engineering, Amity University Haryana, Gurugram 122412, India
Bijeta Seth: Department of Computer Science and Engineering, B. M. Institute of Engineering & Technology, Sonipat 131001, India
Magdalena Radulescu: Department of Finance, Accounting and Economics, University of Pitesti, 110014 Pitesti, Romania
Teodor Florin Cilan: Department of Economics, Aurel Vlaicu University of Arad, 310032 Arad, Romania
Luminita Serbanescu: Department of Finance, Accounting and Economics, University of Pitesti, 110014 Pitesti, Romania

Sustainability, 2023, vol. 15, issue 7, 1-18

Abstract: Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time.

Keywords: deep learning; weather classification; accidents; sustainable transportation; traffic safety; weather challenges; accuracy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/7/6070/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/7/6070/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:7:p:6070-:d:1113058

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().