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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.10096 |
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| _version_ | 1866916437650571264 |
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| author | Pérez, Santiago Gómez, Camila Rodríguez, Matías |
| author_facet | Pérez, Santiago Gómez, Camila Rodríguez, Matías |
| contents | This study explores a comprehensive approach to obstacle detection using advanced YOLO models, specifically YOLOv8, YOLOv7, YOLOv6, and YOLOv5. Leveraging deep learning techniques, the research focuses on the performance comparison of these models in real-time detection scenarios. The findings demonstrate that YOLOv8 achieves the highest accuracy with improved precision-recall metrics. Detailed training processes, algorithmic principles, and a range of experimental results are presented to validate the model's effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms Pérez, Santiago Gómez, Camila Rodríguez, Matías Computer Vision and Pattern Recognition Robotics This study explores a comprehensive approach to obstacle detection using advanced YOLO models, specifically YOLOv8, YOLOv7, YOLOv6, and YOLOv5. Leveraging deep learning techniques, the research focuses on the performance comparison of these models in real-time detection scenarios. The findings demonstrate that YOLOv8 achieves the highest accuracy with improved precision-recall metrics. Detailed training processes, algorithmic principles, and a range of experimental results are presented to validate the model's effectiveness. |
| title | Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2410.10096 |