محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | Recurso digital |
| اللغة: | |
| منشور في: |
Zenodo
2025
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| الوصول للمادة أونلاين: | https://doi.org/10.5281/zenodo.15569355 |
| الوسوم: |
إضافة وسم
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| _version_ | 1866901938306547712 |
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| author | Axmedov Abdulazizxon Ganijon O'g'li Dadaxanov Musoxon Xoshimxonovich |
| author_facet | Axmedov Abdulazizxon Ganijon O'g'li Dadaxanov Musoxon Xoshimxonovich |
| contents | <p><em><span lang="EN-US">Autonomous driving systems rely heavily on camera-based perception, making image quality a critical factor in ensuring accurate decision-making. Lens defects, such as dirt, cracks, and scratches, can significantly degrade image quality, leading to poor performance in object detection and navigation tasks. This study presents an AI-based framework for detecting and assessing lens defects in autonomous vehicle cameras. By combining traditional image processing techniques with deep learning models, including a custom-trained YOLOv8, we demonstrate an effective approach to identifying image distortions. Our findings highlight the strengths and limitations of different detection methods and provide insights into improving defect detection in future implementations.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15569355 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ENHANCING AUTONOMOUS DRIVING SYSTEMS WITH AI-BASED IMAGE QUALITY ASSESSMENT FOR LENS DEFECT DETECTION Axmedov Abdulazizxon Ganijon O'g'li Dadaxanov Musoxon Xoshimxonovich <p><em><span lang="EN-US">Autonomous driving systems rely heavily on camera-based perception, making image quality a critical factor in ensuring accurate decision-making. Lens defects, such as dirt, cracks, and scratches, can significantly degrade image quality, leading to poor performance in object detection and navigation tasks. This study presents an AI-based framework for detecting and assessing lens defects in autonomous vehicle cameras. By combining traditional image processing techniques with deep learning models, including a custom-trained YOLOv8, we demonstrate an effective approach to identifying image distortions. Our findings highlight the strengths and limitations of different detection methods and provide insights into improving defect detection in future implementations.</span></em></p> |
| title | ENHANCING AUTONOMOUS DRIVING SYSTEMS WITH AI-BASED IMAGE QUALITY ASSESSMENT FOR LENS DEFECT DETECTION |
| url | https://doi.org/10.5281/zenodo.15569355 |