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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 i...