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Mine-JEPA: In-Domain Self-Supervised Learning for Mine-Like Object Classification in Side-Scan Sonar

Taeyoun Kwon, Youngwon Choi, Hyeonyu Kim, Myeongkyun Cho, Junhyeok Choi, Moon Hwan Kim

Abstract

Side-scan sonar (SSS) mine classification is a challenging maritime vision problem characterized by extreme data scarcity and a large domain gap from natural images. While self-supervised learning (SSL) and general-purpose vision foundation models have shown strong performance in general vision and several specialized domains, their use in SSS remains largely unexplored. We present Mine-JEPA, the first in-domain SSL pipeline for SSS mine classification, using SIGReg, a regularization-based SSL loss, to pretrain on only 1,170 unlabeled sonar images. In the binary mine vs. non-mine setting, Mine-JEPA achieves an F1 score of 0.935, outperforming fine-tuned DINOv3 (0.922), a foundation model pretrained on 1.7B images. For 3-class mine-like object classification, Mine-JEPA reaches 0.820 with synthetic data augmentation, again outperforming fine-tuned DINOv3 (0.810). We further observe that applying in-domain SSL to foundation models degrades performance by 10--13 percentage points, suggesting that stronger pretrained models do not always benefit from additional domain adaptation. In addition, Mine-JEPA with a compact ViT-Tiny backbone achieves competitive performance while using 4x fewer parameters than DINOv3. These results suggest that carefully designed in-domain self-supervised learning is a viable alternative to much larger foundation models in data-scarce maritime sonar imagery.

Mine-JEPA: In-Domain Self-Supervised Learning for Mine-Like Object Classification in Side-Scan Sonar

Abstract

Side-scan sonar (SSS) mine classification is a challenging maritime vision problem characterized by extreme data scarcity and a large domain gap from natural images. While self-supervised learning (SSL) and general-purpose vision foundation models have shown strong performance in general vision and several specialized domains, their use in SSS remains largely unexplored. We present Mine-JEPA, the first in-domain SSL pipeline for SSS mine classification, using SIGReg, a regularization-based SSL loss, to pretrain on only 1,170 unlabeled sonar images. In the binary mine vs. non-mine setting, Mine-JEPA achieves an F1 score of 0.935, outperforming fine-tuned DINOv3 (0.922), a foundation model pretrained on 1.7B images. For 3-class mine-like object classification, Mine-JEPA reaches 0.820 with synthetic data augmentation, again outperforming fine-tuned DINOv3 (0.810). We further observe that applying in-domain SSL to foundation models degrades performance by 10--13 percentage points, suggesting that stronger pretrained models do not always benefit from additional domain adaptation. In addition, Mine-JEPA with a compact ViT-Tiny backbone achieves competitive performance while using 4x fewer parameters than DINOv3. These results suggest that carefully designed in-domain self-supervised learning is a viable alternative to much larger foundation models in data-scarce maritime sonar imagery.

Paper Structure

This paper contains 17 sections, 2 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Side-Scan Sonar Mine Classification
  4. Self-Supervised Learning for Vision
  5. Domain-Specific SSL & the Remaining Gap
  6. Method
  7. Problem Formulation & Pipeline
  8. SIGReg Self-Supervised Learning
  9. Mine-JEPA: SSS-Adapted SSL
  10. Experiments
  11. Dataset & Setup
  12. Main Results
  13. Analysis
  14. Initialization & Adaptation Behavior
  15. SSL Method Comparison & Data Scalability
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Performance comparison across model scales. Using only 1,170 sonar images for pretraining, Mine-JEPA outperforms DINOv3, pretrained on 1.7B images, at both backbone scales. ViT-Tiny remains competitive with $4\times$ fewer parameters.
  • Figure 2: Overview of the Mine-JEPA pipeline. Stage 1 extracts unlabeled $96\times96$ patches from full SSS images using a sliding window. Stage 2 performs in-domain self-supervised pretraining with SSS-adapted multi-view augmentation and the SIGReg objective. Stage 3 evaluates the pretrained backbone with frozen or fine-tuned probes for downstream patch classification.
  • Figure 3: Representative samples from the public side-scan sonar dataset. Top: a full SSS image with annotated mine-like contacts (MILCO) and non-mine bottom objects (NOMBO). Bottom: $96\times96$ patches from the three classes used in our evaluation: MILCO, background (BG), and NOMBO.