RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels

• URL: http://arxiv.org/abs/2601.12715v1
• Date: Mon, 19 Jan 2026 04:37:34 GMT
• Title: RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels
• Authors: Chengzhou Li, Ping Guo, Guanchen Meng, Qi Jia, Jinyuan Liu, Zhu Liu, Xiaokang Liu, Yu Liu, Zhongxuan Luo, Xin Fan,
• Abstract summary: Object detection in sonar images is a key technology in underwater detection systems.<n>We propose a teacher-student framework called RSOD to fully learn the characteristics of sonar images.<n>We introduce an object mixed pseudo-label method to tackle the shortage of labeled data in sonar images.
• Score: 31.951604817203656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection in sonar images is a key technology in underwater detection systems. Compared to natural images, sonar images contain fewer texture details and are more susceptible to noise, making it difficult for non-experts to distinguish subtle differences between classes. This leads to their inability to provide precise annotation data for sonar images. Therefore, designing effective object detection methods for sonar images with extremely limited labels is particularly important. To address this, we propose a teacher-student framework called RSOD, which aims to fully learn the characteristics of sonar images and develop a pseudo-label strategy suitable for these images to mitigate the impact of limited labels. First, RSOD calculates a reliability score by assessing the consistency of the teacher's predictions across different views. To leverage this score, we introduce an object mixed pseudo-label method to tackle the shortage of labeled data in sonar images. Finally, we optimize the performance of the student by implementing a reliability-guided adaptive constraint. By taking full advantage of unlabeled data, the student can perform well even in situations with extremely limited labels. Notably, on the UATD dataset, our method, using only 5% of labeled data, achieves results that can compete against those of our baseline algorithm trained on 100% labeled data. We also collected a new dataset to provide more valuable data for research in the field of sonar.

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