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Applied Sciences
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Objective Recognition and Detection in Marine Engineering
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Objective Recognition and Detection in Marine Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 807

Editors

Dr. Shigang Wang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
Interests: aeromagnetic survey; magnetic anomaly detection; visual saliency modeling; sar target detection; machine learning; deep neural networks
Dr. Ke Yang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China
Interests: magnetic targeting; magnetic communication; ELF field analysis; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Object detection and recognition play a crucial role in marine engineering, which can facilitate real-time target sensing and information acquisition on both the sea surface and underwater. Currently, various high-performance sensors and their carrying platforms are being developed to collect information on objects of interest in marine observation scenarios. By comprehensively exploiting the physical properties of marine targets, different types of marine observation data can be collected for interpreting and understanding tasks. Due to the limitations of a single physical-field sensor, interferences from sensor-carrying platforms, noises of the surrounding marine environment, and the complex cognitive patterns of marine targets, fast and accurate detection and recognition of marine objects are still ongoing issues. Recently, the rapid development of deep learning techniques has brought new inspiration to this field, and new models and algorithms have been proposed to better tackle the abovementioned challenges. Hopefully, this will further promote the technical development of object detection and recognition in marine engineering.

For this Special Issue, we are therefore interested in receiving articles that investigate marine object detection and recognition by means of advanced sensor and interpretation techniques. Potential research topics include, but are not limited to, the following:

  • Deep learning for marine target detection and recognition;
  • Small-sample learning and feature knowledge transfer;
  • Multi-sensor data fusion for marine target sensing;
  • Calibration and compensation of sensor measurement error;
  • Suppression and elimination of carrying platform interference;
  • Marine noise characteristic analysis and denoising;
  • Spaceborne and airborne marine target surveillance;
  • Underwater acoustic detection and recognition;
  • Aeromagnetic survey for marine target detection;
  • Synthetic aperture radar for surface ship detection;
  • Magnetic anomaly detection and localization;
  • Unmanned underwater vehicle for target detection.

Dr. Shigang Wang
Dr. Yang Ke
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • marine remote sensing
  • deep learning
  • target detection and recognition
  • feature extraction and fusion
  • noise interference suppression
  • image and signal processing

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Published Papers (2 papers)

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Research

26 pages, 5445 KB  
Article
Spectral Denoising and Line Spectrum Extraction for Low-Frequency Underwater Acoustic Signals
by Rui Xiang, Jie Yang, Ke Wang, Tianxiang He, Jinsong Xia, Junlin Zhou, Yan Fu and Duanbing Chen
Appl. Sci. 2026, 16(13), 6400; https://doi.org/10.3390/app16136400 - 26 Jun 2026
Viewed by 234
Abstract
In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep [...] Read more.
In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep learning-integrated framework based on application-oriented integration and adaptation of established techniques tailored to the underwater acoustic domain. The framework consists of the following: (1) the Line Spectrum Separation Network (LSS-Net), which integrates a Time–Frequency Joint LSTM and a Temporal Gated Cross-Attention (TGCA) module within an encoder–decoder architecture adapted for high-resolution underwater acoustic time–frequency spectra; (2) a physics-informed signal simulation approach that realistically models Doppler frequency drift and intensity fluctuations; and (3) a Peak-Tracking Line Extractor (PTLE) algorithm that leverages underwater acoustic-specific temporal constraints. The proposed framework achieves an MOTA of 0.89 on simulated data and 0.52 on real sea trial data, outperforming existing methods by 0.06-2.14 in MOTA and significantly suppressing high-resolution background noise. Full article
(This article belongs to the Special Issue Objective Recognition and Detection in Marine Engineering)
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35 pages, 31827 KB  
Article
DN-AnchorNet: A Unified Framework with Structure-Preserving Enhancement and Adaptive Anchors for Robust Coastal SAR Ship Detection
by Yongqi Kang and Haiping Qu
Appl. Sci. 2026, 16(12), 6184; https://doi.org/10.3390/app16126184 - 18 Jun 2026
Viewed by 299
Abstract
Ship detection in synthetic aperture radar (SAR) imagery, an indispensable all-weather technology for marine engineering and coastal safety, remains challenging in complex nearshore scenes due to coupled speckle noise, sea–land clutter, large scale variation, and extreme class imbalance. Existing decoupled pipelines fail to [...] Read more.
Ship detection in synthetic aperture radar (SAR) imagery, an indispensable all-weather technology for marine engineering and coastal safety, remains challenging in complex nearshore scenes due to coupled speckle noise, sea–land clutter, large scale variation, and extreme class imbalance. Existing decoupled pipelines fail to jointly mitigate these degradations, leading to high false alarm rates and poor generalization. We propose DN-AnchorNet, an end-to-end unified framework integrating a detection-oriented structure-preserving enhancement branch, a scale-adaptive anchor mechanism, and an adaptive weighted Smooth L1 loss. The detection-guided enhancement branch operates without paired clean data to preserve critical ship structures. The scale-adaptive anchor design enhances matching for small, elongated, and arbitrarily oriented ships, while the tailored loss improves regression robustness through dynamic threshold adjustment and valid positive-sample regression masking under class imbalance. Extensive experiments under the adopted fixed nearshore stress-test protocol of RSDD-SAR and SSDD+ show that DN-AnchorNet achieves the best overall performance among the compared representative oriented object detectors in this evaluation setting, with AP50 values of 0.699 and 0.610, and F1-scores of 0.757 and 0.689, respectively. A strict zero-shot cross-dataset evaluation on HRSID provides supplementary evidence of DN-AnchorNet’s transferability to unseen marine SAR conditions. These results suggest that joint optimization can achieve a favorable accuracy–false-detection balance under challenging nearshore SAR detection conditions. Full article
(This article belongs to the Special Issue Objective Recognition and Detection in Marine Engineering)
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