Semi-Supervised Maritime Object Detection: A Data-Centric Perspective

Semi-supervised object detection (SSOD) has emerged as a promising technique to boost the performance of the detectors, utilizing both labeled and unlabeled data. However, in marine environments, SSOD faces formidable challenges posed by complex conditions like shore-based landscapes, varying ship scales, and diverse weather, which complicate data acquisition and thus the generation of accurate pseudo-labels. To tackle these issues, we propose two novel strategies, depth-aware pseudo-label filtering (DAPF) and dynamic region mixup (DRMix) augmentation, from a data-centric perspective. Specifically, the DAPF strategy incorporates depth information as a prior to refine pseudo-labels by filtering out unreliable ones, thereby improving the quality of pseudo-label–data pairs used for training. Meanwhile, DRMix augmentation dynamically mixes images at the regional level, generating diverse and representative data suitable for maritime object detection tasks. Extensive experiments on maritime datasets validate the effectiveness of our approach, achieving mAP improvements of 2.2% on the SeaShips dataset and 0.9% on the Singapore Maritime dataset compared to other state-of-the-art (SOTA) methods. Our code will be made publicly available.