Unsupervised underwater shipwreck detection in side-scan sonar images based on domain-adaptive techniques

Abstract

Abstract Underwater object detection based on side-scan sonar (SSS) suffers from a lack of finely annotated data. This study aims to avoid the laborious task of annotation by achieving unsupervised underwater object detection through domain-adaptive object detection (DAOD). In DAOD, there exists a conflict between feature transferability and discriminability, suppressing the detection performance. To address this challenge, a domain collaborative bridging detector (DCBD) including intra-domain consistency constraint (IDCC) and domain collaborative bridging (DCB), is proposed. On one hand, previous static domain labels in adversarial-based methods hinder the domain discriminator from discerning subtle intra-domain discrepancies, thus decreasing feature transferability. IDCC addresses this by introducing contrastive learning to refine intra-domain similarity. On the other hand, DAOD encourages the feature extractor to extract domain-invariant features, overlooking potential discriminative signals embedded within domain attributes. DCB addresses this by complementing domain-invariant features with domain-relevant information, thereby bolstering feature discriminability. The feasibility of DCBD is validated using unlabeled underwater shipwrecks as a case study. Experiments show that our method achieves accuracy comparable to fully supervised methods in unsupervised SSS detection (92.16% AP50 and 98.50% recall), and achieves 52.6% AP50 on the famous benchmark dataset Foggy Cityscapes, exceeding the original state-of-the-art by 4.5%.