Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions
Accurate and precise detection of lanes and traffic signs is predominant for the safety and efficiency of autonomous vehicles and these two significant tasks should be addressed to handle Indian traffic conditions. There are several state-of-art You Only Live Once (YOLO) models trained on benchmark...
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000263 |
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| author | H. S. Gowri Yaamini Swathi K J Manohar N Ajay Kumar G |
| author_facet | H. S. Gowri Yaamini Swathi K J Manohar N Ajay Kumar G |
| author_sort | H. S. Gowri Yaamini |
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| description | Accurate and precise detection of lanes and traffic signs is predominant for the safety and efficiency of autonomous vehicles and these two significant tasks should be addressed to handle Indian traffic conditions. There are several state-of-art You Only Live Once (YOLO) models trained on benchmark datasets which fails to cater the challenges of Indian roads. To address these issues, the models need to be trained with a wide variety of Indian data samples for the autonomous vehicles to perform better in India. YOLOv8 algorithm has its challenges but gives better precision results and YOLOv8 nano variant is widely used as it is computationally less complex comparatively. Through rigorous evaluations of diverseness in the datasets, the proposed YOLOv8n transfer learning models exhibits remarkable performance with a mean Average Precision (mAP) of 90.6 % and inference speed of 117 frames per second (fps) for lane detection whereas, a notable mAP of 81.3 % for traffic sign detection model with a processing speed of 56 fps. • YOLOv8n Transfer Learning approach by adjusting architecture for lane and traffic sign detection in Indian diverse Urban, Suburban, and Highway scenarios. • Dataset with 22,400 images of normal and complex Indian scenarios include crude weathering of roads, traffic conditions, diverse tropical weather conditions, partially occluded and partially erased lanes, and traffic signs. • The model performance with notable precision and frame wise inference. |
| format | Article |
| id | doaj-art-c7cc1aa1f53047ae9acbbe4db28c3b0c |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-c7cc1aa1f53047ae9acbbe4db28c3b0c2025-01-24T04:45:16ZengElsevierMethodsX2215-01612025-06-0114103178Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road ConditionsH. S. Gowri Yaamini0Swathi K J1Manohar N2Ajay Kumar G3Department of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru 570026, IndiaDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru 570026, IndiaDepartment of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru 570026, India; Corresponding author.Department of Mining and Explosive, Engineering Missouri University of Science and Technology, MO 65409-1060, USAAccurate and precise detection of lanes and traffic signs is predominant for the safety and efficiency of autonomous vehicles and these two significant tasks should be addressed to handle Indian traffic conditions. There are several state-of-art You Only Live Once (YOLO) models trained on benchmark datasets which fails to cater the challenges of Indian roads. To address these issues, the models need to be trained with a wide variety of Indian data samples for the autonomous vehicles to perform better in India. YOLOv8 algorithm has its challenges but gives better precision results and YOLOv8 nano variant is widely used as it is computationally less complex comparatively. Through rigorous evaluations of diverseness in the datasets, the proposed YOLOv8n transfer learning models exhibits remarkable performance with a mean Average Precision (mAP) of 90.6 % and inference speed of 117 frames per second (fps) for lane detection whereas, a notable mAP of 81.3 % for traffic sign detection model with a processing speed of 56 fps. • YOLOv8n Transfer Learning approach by adjusting architecture for lane and traffic sign detection in Indian diverse Urban, Suburban, and Highway scenarios. • Dataset with 22,400 images of normal and complex Indian scenarios include crude weathering of roads, traffic conditions, diverse tropical weather conditions, partially occluded and partially erased lanes, and traffic signs. • The model performance with notable precision and frame wise inference.http://www.sciencedirect.com/science/article/pii/S2215016125000263YOLOv8 Transfer Learning |
| spellingShingle | H. S. Gowri Yaamini Swathi K J Manohar N Ajay Kumar G Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions MethodsX YOLOv8 Transfer Learning |
| title | Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions |
| title_full | Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions |
| title_fullStr | Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions |
| title_full_unstemmed | Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions |
| title_short | Lane and Traffic Sign Detection for Autonomous Vehicles: Addressing Challenges on Indian Road Conditions |
| title_sort | lane and traffic sign detection for autonomous vehicles addressing challenges on indian road conditions |
| topic | YOLOv8 Transfer Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000263 |
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