A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments

Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing accept...

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Bibliographic Details
Published in:Applied sciences 2023-03, Vol.13 (5), p.3059
Main Authors: Farid, Annam, Hussain, Farhan, Khan, Khurram, Shahzad, Mohsin, Khan, Uzair, Mahmood, Zahid
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Classification
Concept learning
Datasets
Deep learning
Intelligent transportation systems
Localization
Machine learning
Methods
Neural networks
object detection
Roads & highways
Simulation methods
Surveillance
Traffic congestion
Traffic control
Transfer learning
vehicle detection
Vehicles
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Summary:Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultimately, the improved YOLO-v5 structure becomes accustomed to any difficult traffic patterns. By fine-tuning the pre-trained network through our datasets, our proposed YOLO-v5 has exceeded several other traditional vehicle detection methods in terms of detection accuracy and execution time. Detailed simulations performed on the PKU, COCO, and DAWN datasets demonstrate the effectiveness of the proposed method in various challenging situations.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13053059