Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study
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Abstract (arXiv)
We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches. Here we answer to questions that are specific to our application including how to utilize DNN for vehicle detection, what features are useful for vehicle classification, and how to extend a model trained on a limited size dataset, to the cases of extreme lighting condition. Answering these questions we propose our approach that outperforms state-of-the-art methods, and achieves promising results on image with extreme lighting conditions.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2016.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladeshcited by 4
- 2023Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigmscited by 1
- 2024Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipelinecited by 0
- 2024Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline3 Jun 2024
- 2023Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms1 Feb 2023
- 2022Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh18 Dec 2022