Detection Datasets

Detection datasets are crucial for training and evaluating object detection models, driving advancements in computer vision. Current research focuses on addressing limitations in existing datasets, including expanding the number of classes (especially rare ones), handling diverse data modalities (e.g., event-based vision, LiDAR point clouds), and improving annotation efficiency through active learning and self-training techniques. These efforts are improving the accuracy and robustness of object detectors, with applications ranging from autonomous driving and industrial automation to medical image analysis and environmental monitoring. The development of larger, more diverse, and efficiently annotated datasets is key to unlocking the full potential of object detection algorithms.