Academic Journal
Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles
| العنوان: | Application and Comparison of Deep Learning Methods to Detect Night-Time Road Surface Conditions for Autonomous Vehicles |
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| المؤلفون: | Zhang, Hongyi, Sehab, Rabia, Azouigui, Sheherazade, Boukhnifer, Moussa |
| المساهمون: | École Supérieure des Techniques Aéronautiques et de Construction Automobile (ESTACA), ESTACA'Lab ( ESTACA'Lab ), Institut d'Optique Graduate School (IOGS), Laboratoire de Conception, Optimisation et Modélisation des Systèmes (LCOMS), Université de Lorraine (UL) |
| المصدر: | ISSN: 2079-9292 ; Electronics ; https://hal.science/hal-04491767 ; Electronics, 2022, 11 (5), pp.786. ⟨10.3390/electronics11050786⟩. |
| بيانات النشر: | HAL CCSD MDPI |
| سنة النشر: | 2022 |
| المجموعة: | Université de Lorraine: HAL |
| مصطلحات موضوعية: | autonomous vehicles, driving safety, night-time detection, road surface conditions, features, deep learning, accuracy, performance, [SPI]Engineering Sciences [physics] |
| الوصف: | International audience ; Currently, road surface conditions ahead of autonomous vehicles are not well detected by the existing sensors on those autonomous vehicles. However, driving safety should be ensured for the weather-induced road conditions for day and night. An investigation into deep learning to recognize the road surface conditions in the day is conducted using the collected data from an embedded camera on the front of the vehicles. Deep learning models have only been proven to be successful in the day, but they have not been assessed for night conditions to date. The objective of this work is to propose deep learning models to detect on-line road surface conditions caused by weather ahead of the autonomous vehicles at night with a high accuracy. For this study, different deep learning models, namely traditional CNN, SqueezeNet, VGG, ResNet, and DenseNet models, are applied with performance comparison. Considering the current limitation of existing night-time detection, reflection features of different road surfaces are investigated in this paper. According to the features, night-time databases are collected with and without ambient illumination. These databases are collected from several public videos in order to make the selected models more applicable to more scenes. In addition, selected models are trained based on a collected database. Finally, in the validation, the accuracy of these models to classify dry, wet, and snowy road surface conditions at night can be up to 94%. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| Relation: | hal-04491767; https://hal.science/hal-04491767; https://hal.science/hal-04491767/document; https://hal.science/hal-04491767/file/electronics-11-00786-v2.pdf |
| DOI: | 10.3390/electronics11050786 |
| الاتاحة: | https://hal.science/hal-04491767 https://hal.science/hal-04491767/document https://hal.science/hal-04491767/file/electronics-11-00786-v2.pdf https://doi.org/10.3390/electronics11050786 |
| Rights: | http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.66F9D10E |
| قاعدة البيانات: | BASE |
| DOI: | 10.3390/electronics11050786 |
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