Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada
The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suit...
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| Format: | Article |
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10399478/ |
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| author | Teena Sharma Abdellah Chehri Issouf Fofana Shubham Jadhav Siddhartha Khare Benoit Debaque Nicolas Duclos-Hindie Deeksha Arya |
| author_facet | Teena Sharma Abdellah Chehri Issouf Fofana Shubham Jadhav Siddhartha Khare Benoit Debaque Nicolas Duclos-Hindie Deeksha Arya |
| author_sort | Teena Sharma |
| collection | DOAJ |
| description | The rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions. |
| format | Article |
| id | doaj-art-b2dd244a769f4d2b8262033ae8e5ed41 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b2dd244a769f4d2b8262033ae8e5ed412025-08-20T02:26:27ZengIEEEIEEE Access2169-35362024-01-0112136481366210.1109/ACCESS.2024.335407610399478Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, CanadaTeena Sharma0https://orcid.org/0000-0002-9002-0595Abdellah Chehri1https://orcid.org/0000-0002-4193-6062Issouf Fofana2https://orcid.org/0000-0001-7141-9173Shubham Jadhav3https://orcid.org/0009-0006-2877-8166Siddhartha Khare4https://orcid.org/0000-0002-6996-4583Benoit Debaque5Nicolas Duclos-Hindie6Deeksha Arya7https://orcid.org/0000-0002-7948-5930Department of Applied Sciences (DSA), University of Quebec at Chicoutimi, Saguenay, QC, CanadaDepartment of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON, CanadaDepartment of Applied Sciences (DSA), University of Quebec at Chicoutimi, Saguenay, QC, CanadaDepartment of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, IndiaDepartment of Civil Engineering, Geomatics Engineering Section, Indian Institute of Technology Roorkee, Roorkee, IndiaThales Research and Technology, Quebec, QC, CanadaThales Research and Technology, Quebec, QC, CanadaCentre for Spatial Information Science, University of Tokyo, Tokyo, JapanThe rapid development of self-driving vehicles requires integrating a sophisticated sensing system to address the various obstacles posed by road traffic efficiently. While several datasets are available to support object detection in autonomous vehicles, it is crucial to carefully evaluate the suitability of these datasets for different weather conditions across the globe. In response to this requirement, we present a novel dataset named the Canadian Vehicle Datasets (CVD). Subsequently, we present deep learning models that use this dataset. The CVD comprises street-level videos which were recorded by Thales, Canada. These videos were collected with high-quality cameras mounted on a vehicle in the Canadian province of Quebec. The recordings were made during daytime and nighttime, capturing weather conditions such as hazy, snowy, rainy, gloomy, nighttime and sunny days. A total of 10000 images of vehicles and other road assets are extracted from the collected videos. A total of 8388 images were annotated with corresponding generated labels 27766 with their respective 11 different classes. We analyzed the performance of the YOLOv8 model trained using the existing RoboFlow dataset. Then, we compared it with the model trained on the expanded version of RoboFlow using the proposed weather-specific dataset, CVD. Final values of improved accuracy of 73.26 %, 72.84 %, and 73.47 % (Precision/Recall/mAP) were reported upon adding the proposed dataset. Finally, the model trained on this diverse dataset exhibits heightened robustness and proves highly beneficial for both autonomous and conventional vehicle operations, making it applicable not only in Canada but also in other countries with comparable weather conditions.https://ieeexplore.ieee.org/document/10399478/Autonomous vehiclesconvolutional neural networksintelligent transportationobject detectorsurveillanceYOLOv8 |
| spellingShingle | Teena Sharma Abdellah Chehri Issouf Fofana Shubham Jadhav Siddhartha Khare Benoit Debaque Nicolas Duclos-Hindie Deeksha Arya Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada IEEE Access Autonomous vehicles convolutional neural networks intelligent transportation object detector surveillance YOLOv8 |
| title | Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada |
| title_full | Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada |
| title_fullStr | Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada |
| title_full_unstemmed | Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada |
| title_short | Deep Learning-Based Object Detection and Classification for Autonomous Vehicles in Different Weather Scenarios of Quebec, Canada |
| title_sort | deep learning based object detection and classification for autonomous vehicles in different weather scenarios of quebec canada |
| topic | Autonomous vehicles convolutional neural networks intelligent transportation object detector surveillance YOLOv8 |
| url | https://ieeexplore.ieee.org/document/10399478/ |
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