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Detection in Adverse Weather Conditions for Autonomous Vehicles via Deep Learning

Department of Computer Science/Cybersecurity, Princess Sumaya University for Technology, Amman 11941, Jordan
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Author to whom correspondence should be addressed.
Academic Editor: Giovanni Diraco
AI 2022, 3(2), 303-317; https://doi.org/10.3390/ai3020019
Received: 15 March 2022 / Revised: 13 April 2022 / Accepted: 14 April 2022 / Published: 18 April 2022
(This article belongs to the Special Issue Feature Papers for AI)
Weather detection systems (WDS) have an indispensable role in supporting the decisions of autonomous vehicles, especially in severe and adverse circumstances. With deep learning techniques, autonomous vehicles can effectively identify outdoor weather conditions and thus make appropriate decisions to easily adapt to new conditions and environments. This paper proposes a deep learning (DL)-based detection framework to categorize weather conditions for autonomous vehicles in adverse or normal situations. The proposed framework leverages the power of transfer learning techniques along with the powerful Nvidia GPU to characterize the performance of three deep convolutional neural networks (CNNs): SqueezeNet, ResNet-50, and EfficientNet. The developed models have been evaluated on two up-to-date weather imaging datasets, namely, DAWN2020 and MCWRD2018. The combined dataset has been used to provide six weather classes: cloudy, rainy, snowy, sandy, shine, and sunrise. Experimentally, all models demonstrated superior classification capacity, with the best experimental performance metrics recorded for the weather-detection-based ResNet-50 CNN model scoring 98.48%, 98.51%, and 98.41% for detection accuracy, precision, and sensitivity. In addition to this, a short detection time has been noted for the weather-detection-based ResNet-50 CNN model, involving an average of 5 (ms) for the time-per-inference step using the GPU component. Finally, comparison with other related state-of-art models showed the superiority of our model which improved the classification accuracy for the six weather conditions classifiers by a factor of 0.5–21%. Consequently, the proposed framework can be effectively implemented in real-time environments to provide decisions on demand for autonomous vehicles with quick, precise detection capacity. View Full-Text
Keywords: deep learning (DL); convolutional neural network (CNN); transfer learning; SqueezeNet CNN; ResNet-50 CNN; EfficientNet-b0 CNN; weather conditions; autonomous vehicles; image classification deep learning (DL); convolutional neural network (CNN); transfer learning; SqueezeNet CNN; ResNet-50 CNN; EfficientNet-b0 CNN; weather conditions; autonomous vehicles; image classification
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MDPI and ACS Style

Al-Haija, Q.A.; Gharaibeh, M.; Odeh, A. Detection in Adverse Weather Conditions for Autonomous Vehicles via Deep Learning. AI 2022, 3, 303-317. https://doi.org/10.3390/ai3020019

AMA Style

Al-Haija QA, Gharaibeh M, Odeh A. Detection in Adverse Weather Conditions for Autonomous Vehicles via Deep Learning. AI. 2022; 3(2):303-317. https://doi.org/10.3390/ai3020019

Chicago/Turabian Style

Al-Haija, Qasem A., Manaf Gharaibeh, and Ammar Odeh. 2022. "Detection in Adverse Weather Conditions for Autonomous Vehicles via Deep Learning" AI 3, no. 2: 303-317. https://doi.org/10.3390/ai3020019

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