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Real-time underwater object detection via frequency-domain dynamics and spatially enhanced feature modulation.

Shaobin Cai1, Aocheng Zhu2

  • 1College of Information Engineering, Huzhou University, Huzhou, 313000, China. caishaobin@zjhu.edu.cn.

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Summary

This study introduces a lightweight deep learning framework for underwater object detection, improving accuracy by 1.7% while significantly reducing model size and increasing speed for real-time marine engineering applications.

Keywords:
Feature modulationFrequency-domain Dynamic ConvolutionLightweight networkRT-DETRUnderwater object detection

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Area of Science:

  • Computer Vision
  • Marine Engineering
  • Deep Learning

Background:

  • Underwater object detection faces challenges due to poor visibility, low contrast, and texture loss.
  • Deploying deep learning models on resource-constrained platforms requires balancing accuracy and inference speed.

Purpose of the Study:

  • To propose a novel, lightweight deep learning framework for efficient and accurate underwater object detection.
  • To enhance feature extraction and mitigate detail loss in complex underwater environments.

Main Methods:

  • Developed a FasterFDBlock backbone using Partial Convolution and Frequency-domain Dynamic Convolution for adaptive noise suppression and edge enhancement.
  • Introduced an AIFI-SEFN encoder with a Spatially Enhanced Feed-Forward Network to integrate global and local features.
  • Implemented a Multi-scale Feature Modulation (MFM) module for dynamic feature weighting to improve robustness against scale variations and interference.

Main Results:

  • Achieved a mean Average Precision (mAP) of 72.1% on the UTDAC2020 dataset, outperforming the baseline by 1.7%.
  • Reduced model parameters by 27.1% and GFLOPs by 24.6%.
  • Attained an inference speed of 72.6 FPS, demonstrating high efficiency.

Conclusions:

  • The proposed lightweight framework offers a significant improvement in accuracy and efficiency for real-time underwater object detection.
  • The novel backbone, encoder, and modulation modules effectively address challenges in underwater imaging and model deployment constraints.