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Cited by 6Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningVideo Surveillance and Tracking Methods

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

YOLO Algorithms Performance Comparison for Object Detection in Adverse Weather Conditions

Haimer, Zineb, Mateur, Khalid, Farhan, Youssef, Madi, Abdessalam Ait·2023·2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)
DOI 10.1109/iraset57153.2023.10152924
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Abstract

This paper aimed to evaluate and compare the performance of various versions of the YOLO object detection (OD) algorithm under adverse weather conditions, including rain, fog, snow, and sand, during both day and night. A dataset of images captured in these conditions was used to carry out the performance, in terms of OD, of the previous last five versions of YOLO (You Only Look Once), using a cloud-based platform, named Google Colab Notebook. The obtained results showed that YOLOv7 outperformed the other versions in terms of both speed and accuracy. It was the fastest algorithm, completing OD in under 17.4 milliseconds, and it had the highest detection rate for the most classes in the dataset. These findings suggest that YOLOv7 is the best option for OD in adverse weather conditions and under challenging lighting conditions.

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Research works printed but not linked: 31

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Metadata

Authors
Haimer, Z., Mateur, K., Farhan, Y., & Madi, A. A.
Affiliations
  • Université Ibn-Tofail (MA)
  • National School of Applied Science, Ibn Tofail University,Advanced Systems Engineering Laboratory,Kenitra,Morocco
ORCID
Haimer, Zineb
Venue
2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)
Publisher
IEEE
Published
2023-05-18
Pages
1-7
DOI
10.1109/iraset57153.2023.10152924
OpenAlex
W4381744548
Cited by
6
References
4
Citations by year
2025: 1 · 2024: 3 · 2023: 2
FWCI
0.64
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Keywords
YOLO, object detection, severe weather, YOLOv7, YOLOv5, YOLOv4, YOLOv3, YOLOv2, rain, fog, snow, sand, night vision, detection speed and accuracy, performance comparison

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