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An Intelligent Ship Detection Algorithm Based on Visual Sensor Signal Processing for AIoT-Enabled Maritime

Liang Zhang1, Yueqiu Jiang2, Wei Yang3

  • 1Graduate School, Shenyang Ligong University, Shenyang 110159, China.

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This summary is machine-generated.

This study introduces JAOSD, an advanced AI system for detecting ships in maritime surveillance. It significantly improves accuracy and speed in real-time visual stream analysis.

Keywords:
artificial intelligence of thingsautomated maritime surveillanceoriented bounding boxsensor data analysisship detectionsignal processing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence of Things (AIoO)
  • Maritime Surveillance

Background:

  • Oriented object detection is crucial for AIoT maritime surveillance but faces challenges with current methods.
  • Existing detectors struggle with spatial-channel dependencies, unstable predictions for elongated objects, and inaccurate sample assignment due to ignored angular alignment.

Purpose of the Study:

  • To develop an improved anchor-free framework for oriented object detection in maritime surveillance.
  • To address limitations in attention mechanisms, deformable convolutions, and distance metrics in existing detectors.

Main Methods:

  • Proposed JAOSD (Joint Attention-based Oriented Ship Detection) framework.
  • Introduced a joint attention module for parallel spatial-channel processing with coupled fusion.
  • Developed adaptive geometric convolution with two-stage offset refinement and spatial consistency regularization.
  • Implemented an orientation-aware Adaptive Sample Selection strategy using corner-aware distance metrics.

Main Results:

  • Achieved state-of-the-art performance on HRSC2016 (94.74% mAP), FGSD2021 (92.43% AP50), and DOTA v1.0 (80.44% mAP).
  • Maintained real-time inference speed at 42.6 FPS.
  • Demonstrated robust cross-domain generalization from aerial to shore-based surveillance on the Singapore Maritime Dataset.

Conclusions:

  • JAOSD effectively overcomes limitations of existing oriented object detection methods.
  • The proposed framework offers superior performance and real-time capabilities for AIoT maritime surveillance.
  • JAOSD shows strong generalization, making it suitable for diverse surveillance scenarios.