Traffic Signboard Detection Dataset for Object Detection Models

This dataset contains 1,475 labeled images of traffic signboards, curated for developing and evaluating object detection and recognition models in the field of computer vision.The dataset includes annotations for 7 classes of traffic signs, commonly found in real-world road environments. Each image contains one or more signboards labeled with bounding boxes suitable for training models such as YOLO, SSD, or Faster R-CNN. Classes and Distribution Class Name Count 30 243 40 48 Converge 6 Left Turn 272 Right Turn 242 School 362 T Shape 364 The dataset exhibits class imbalance, reflecting real-world conditions where certain signs are more frequent than others. Users are encouraged to apply data augmentation or class weighting strategies for balanced model performance. Applications Traffic sign detection and classification Autonomous driving systems Road safety monitoring and analytics Computer vision research and benchmarking Format Image type: JPEG/PNG Annotation format: YOLO-compatible text files (or specify if COCO/XML etc.) Total images: 1,475 Usage This dataset is intended for research and educational purposes. It can be used to train and evaluate deep learning models for small object detection and classification in real-world scenarios.