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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.06864 |
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| _version_ | 1866913830763757568 |
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| author | Muñoz, Andrés Thomas, Nancy Vapsi, Annita Borrajo, Daniel |
| author_facet | Muñoz, Andrés Thomas, Nancy Vapsi, Annita Borrajo, Daniel |
| contents | Many industrial and service sectors require tools to extract vehicle characteristics from images. This is a complex task not only by the variety of noise, and large number of classes, but also by the constant introduction of new vehicle models to the market. In this paper, we present Veri-Car, an information retrieval integrated approach designed to help on this task. It leverages supervised learning techniques to accurately identify the make, type, model, year, color, and license plate of cars. The approach also addresses the challenge of handling open-world problems, where new car models and variations frequently emerge, by employing a sophisticated combination of pre-trained models, and a hierarchical multi-similarity loss. Veri-Car demonstrates robust performance, achieving high precision and accuracy in classifying both seen and unseen data. Additionally, it integrates an ensemble license plate detection, and an OCR model to extract license plate numbers with impressive accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06864 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Veri-Car: Towards Open-world Vehicle Information Retrieval Muñoz, Andrés Thomas, Nancy Vapsi, Annita Borrajo, Daniel Computer Vision and Pattern Recognition Many industrial and service sectors require tools to extract vehicle characteristics from images. This is a complex task not only by the variety of noise, and large number of classes, but also by the constant introduction of new vehicle models to the market. In this paper, we present Veri-Car, an information retrieval integrated approach designed to help on this task. It leverages supervised learning techniques to accurately identify the make, type, model, year, color, and license plate of cars. The approach also addresses the challenge of handling open-world problems, where new car models and variations frequently emerge, by employing a sophisticated combination of pre-trained models, and a hierarchical multi-similarity loss. Veri-Car demonstrates robust performance, achieving high precision and accuracy in classifying both seen and unseen data. Additionally, it integrates an ensemble license plate detection, and an OCR model to extract license plate numbers with impressive accuracy. |
| title | Veri-Car: Towards Open-world Vehicle Information Retrieval |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.06864 |