Adaptive Dynamic Label Assignment for Tiny Object Detection in Aerial Images

Tiny object detection is one of the most difficult and critical tasks in remote sensing intelligent interpretation applications. Compared with standard-size object detection, detecting tiny objects is more challenging as they typically contain fewer pixels. Besides, the metrics based on Intersection-over-Union (IoU) are more sensitive to the their positioning bias. However, current mainstream object detectors usually assign samples to the Ground Truth (GT) according to a fixed IoU threshold, which would lead to a certain number of tiny object fail to be assigned with high IoU conditional anchors as positive sample candidates under a static threshold. Consequently, insufficient positive samples would affect model training to further constrain the detection performance for tiny objects. In this paper, a sample selection strategy called adaptive dynamic label assignment is proposed to optimize the training effectiveness and improve tiny object detection performance. First, sample allocation thresholds are individually assigned for each GT based on their shape, size, and positions on the feature map. Second, the sample sets are dynamically adjusted during training by using a newly designed indicator called dynamic IoU. Finally, with the guidance of this adaptive dynamic label assignment strategy, each GT can acquire sufficient positive samples for practical training. Extensive experiments on the AI-TOD and Levir-Ship datasets show that, compared with the baseline model, the tiny object detectors trained by our proposed adaptive dynamic label assignment strategy can significantly improve the tiny object detection performance without increasing storage space and inference time. Our method exhibits high portability and outperforms the state-of-the-art methods.