ProCreDet: Progressive Credible Sample Selection for Sparsely Annotated Underwater Object Detection

Sparsely Annotated Object Detection (SAOD) is designed to address the high cost of dataset annotation. In underwater scenarios, its inherent challenges are particularly pronounced: variable underwater illumination conditions lead to significant discrepancies in instance features across different images, the mixing of unlabeled positive samples into negative sets disrupts the training logic, and the model tends to generate numerous misclassifications and struggles to effectively preserve the inter-class correlation information of targets. These factors collectively impede the improvement of model performance. To tackle these challenges, we propose ProCreDet, a teacher-student framework based on progressive training with credible samples. This framework employs a complementary feature learning mechanism to handle diverse underwater illumination conditions for positive sample mining, integrates a risky sample filtering module to resolve the issue of unlabeled positive samples being mixed into negative samples, and introduces a conflict mitigation module to suppress false predictions while preserving inter-class relationship information, thereby enhancing the generalization ability of the model.