Lightweight Underwater Sonar Object Detection via RGB-Guided Heterogeneous Distillation
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Le résumé fourni par la source
Underwater object detection is a fundamental task in underwater sensing and is generally approached using either optical or sonar sensors. Although optical imaging provides rich semantic information, it is highly susceptible to water turbidity and illumination variations. By contrast, sonar imaging can effectively overcome visibility limitations, yet it suffers from severe speckle noise and blurred object contours. Moreover, resource-limited platforms impose strict demands on model lightweightness and real-time performance. To this end, this paper proposes a novel cross-modal heterogeneous distillation method (CMHD) to balance detection accuracy and computational complexity. CMHD performs cross-modal knowledge transfer by leveraging the rich semantics of RGB images to enhance sonar feature representation, compensating for the information deficiency of the sonar modality. Meanwhile, a heterogeneous distillation scheme compresses the detection capability of a high-capacity teacher YOLOX-M into a lightweight student YOLOX-S-Ghost, enabling strong feature extraction under a highly compact model. To mitigate the modality gap and geometric inconsistency between RGB and sonar modalities, we design a branch-aware heterogeneous distillation strategy. To improve detection accuracy and reduce model parameters, the student network incorporates Coordinate Attention (CA) in its backbone and adopts a lightweight neck design. Experiments on the UXO† dataset demonstrate that CMHD achieves 79.6% mAP and 82.6% mAR, significantly outperforming the compared representative methods and serving as an accurate, efficient, and lightweight solution for underwater sonar object detection.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Lightweight Underwater Sonar Object Detection via RGB-Guided Heterogeneous Distillation
- Date Crossref
- 08/07/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Nanjing University State Key Laboratory for Novel Software Technology pays non établi dans la noticeUniversité ou école supérieure
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Chinese Academy of Sciences pays non établi dans la noticeOrganisme public
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Institute of Acoustics pays non établi dans la noticeStructure de recherche
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Academy of Military Medical Sciences pays non établi dans la noticeStructure de recherche
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Laboratory of Autonomous Underwater Vehicles pays non établi dans la noticeStructure de recherche
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National Innovation Institute of Defense Technology pays non établi dans la noticeStructure de recherche
State Key Laboratory for Novel Software Technology — Nanjing University, Chinese Academy of Sciences et Institute of Acoustics, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.