Batch-Aware Active Learning for Object Detection

We propose a Batch-Aware Active Learning (BAAL) framework to optimize the training of object detection models, reducing annotation costs while maintaining strong model performance. The framework adapts different uncertainty sampling strategies to the specific challenges of object detection, including multi-class labelling and spatial localization. By combining uncertainty with diversity, leveraging feature representations and clustering, our method ensures diverse and informative batch selection. The non-invasive, plug-and-play design supports seamless integration with any object detection model without architectural modifications. Evaluations on COCO and Pascal VOC datasets with SSD, Faster R-CNN, YOLOv8, and RetinaNet demonstrate that our approach is not only efficient and robust but also comparable to, and in some cases exceeds, current state-of-the-art solutions.