RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels
Chengzhou Li, Ping Guo, Guanchen Meng, Qi Jia, Jinyuan Liu, Zhu Liu, Xiaokang Liu, Yu Liu
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
BibTeX
@inproceedings{aaai2026_rsodreliabilityg,
title = {RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels},
author = {Chengzhou Li and Ping Guo and Guanchen Meng and Qi Jia and Jinyuan Liu and Zhu Liu and Xiaokang Liu and Yu Liu and Zhongxuan Luo and Xin Fan},
booktitle = {AAAI 2026},
year = {2026}
}