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, Zhongxuan Luo, Xin Fan

TL;DR
This paper introduces RSOD, a reliability-guided teacher-student framework for sonar image object detection that effectively utilizes unlabeled data to achieve high performance with minimal labels.
Contribution
The paper proposes a novel pseudo-label strategy and reliability assessment method tailored for sonar images with extremely limited labels.
Findings
RSOD achieves competitive results using only 5% labeled data.
The method effectively leverages unlabeled data for improved detection.
A new sonar dataset was collected for research purposes.
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Underwater Acoustics Research · Domain Adaptation and Few-Shot Learning
