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Cited by 32Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments

Open access
Aloufi, Nasser, Alnori, Abdulaziz, Thayananthan, Vijey, Basuhail, Abdullah·2023·Applied Sciences
DOI 10.3390/app131810249
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Abstract

In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the environment, such as low visibility, occlusion, and changes in lighting. In this paper, we considered the You Only Look Once (YOLO) version 5 and version 7 architectures to evaluate the performance of different activation functions in sandy weather. In our experiments, we targeted three activation functions: Sigmoid Linear Unit (SiLU), Rectified Linear Unit (ReLU), and Leaky Rectified Linear Unit (LeakyReLU). The metrics used to evaluate their performance were precision, recall, and mean average precision (mAP). We used the Detection in Adverse Weather Nature (DAWN) dataset which contains various weather conditions, though we selected sandy images only. Moreover, we extended the DAWN dataset and created an augmented version of the dataset using several augmentation techniques, such as blur, saturation, brightness, darkness, noise, exposer, hue, and grayscale. Our results show that in the original DAWN dataset, YOLOv5 with the LeakyReLU activation function surpassed other architectures with respect to the reported research results in sandy weather and achieved 88% mAP. For the augmented DAWN dataset that we developed, YOLOv7 with SiLU achieved 94% mAP.

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Research works printed but not linked: 10

  1. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv.
  2. Zijiang, 2002, Climate characteristics of sandstorm in China in recent 47 years, J. Appl. Meteor. Sci., 13, 193
  3. Hadj-Bachir, M., de Souza, P., Nordqvist, P., and Roy, N. (2021). Modelling of LIDAR sensor disturbances by solid airborne particles. arXiv.
  4. Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv.
  5. Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16–21). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA.
  6. Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for activation functions. arXiv.
  7. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
  8. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
  9. Kenk, M.A., and Hassaballah, M. (2020). DAWN: Vehicle detection in adverse weather nature dataset. arXiv.
  10. Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2021). You only learn one representation: Unified network for multiple tasks. arXiv.

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Metadata

Authors
Aloufi, N., Alnori, A., Thayananthan, V., & Basuhail, A.
Affiliations
King Abdulaziz University (SA)
Corresponding author
Aloufi, Nasser
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2023-09-13
Volume
13
Issue
18
Pages
10249
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app131810249
OpenAlex
W4386712543
Cited by
27
References
41
Citations by year
2026: 4 · 2025: 9 · 2024: 11 · 2023: 3
FWCI
2.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
object detection, autonomous vehicles, adverse weather, activation functions, YOLOv5, YOLOv7, leaky ReLU, ReLU, data augmentation, low visibility, occlusion

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