Fine Grained Vehicle

Fine-grained vehicle recognition (FGVR) focuses on accurately identifying specific vehicle makes, models, and even parts, going beyond simple categorization. Current research emphasizes robust methods for handling challenging conditions like varying lighting, occlusion, and noise, often employing deep learning architectures such as YOLO and ResNet variants, along with innovative techniques like multi-task learning and data augmentation. Advances in FGVR are crucial for applications ranging from autonomous driving and traffic monitoring to forensic investigations and improved intelligent transportation systems. The development of large, diverse datasets is also a key area of focus, addressing limitations in existing resources.