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An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment.

Hongjie Deng1, Daji Ergu1, Fangyao Liu1

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Summary
This summary is machine-generated.

This study introduces an improved Mask R-CNN algorithm for detecting and segmenting marine garbage using autonomous underwater detectors. The enhanced algorithm significantly improves accuracy in complex underwater environments.

Keywords:
deep learningdilated convolutioninstance segmentationmarine ecologyobject detectionthe attentional mechanism

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Area of Science:

  • Artificial Intelligence
  • Marine Robotics
  • Computer Vision

Background:

  • Autonomous underwater vehicles (AUVs) are crucial for marine debris cleanup.
  • Object detection in complex marine environments presents challenges due to low image resolution and occlusions.
  • Accurate instance segmentation is vital for effective marine garbage identification and removal.

Purpose of the Study:

  • To develop an improved Mask R-CNN algorithm for high-accuracy marine garbage detection and instance segmentation.
  • To enhance the performance of underwater object detection algorithms in challenging marine conditions.
  • To provide a robust solution for autonomous marine garbage cleanup applications.

Main Methods:

  • Implemented an improved Mask R-CNN algorithm incorporating dilated convolution in the Feature Pyramid Network (FPN) for enhanced small object feature extraction.
  • Integrated a spatial-channel attention mechanism to adaptively focus on relevant features for improved detection.
  • Added a re-scoring branch utilizing Generalized Intersection over Union (GIoU) to refine instance segmentation accuracy.

Main Results:

  • The proposed algorithm demonstrated significant improvements over the baseline on the Transcan dataset.
  • Achieved a 9.6% increase in mean Average Precision (mAP) for garbage detection.
  • Achieved a 5.0% increase in mAP for instance segmentation, showcasing enhanced performance.

Conclusions:

  • The improved Mask R-CNN algorithm offers superior performance for marine garbage detection and instance segmentation in complex underwater environments.
  • The algorithm's enhancements, including dilated convolution, attention mechanisms, and GIoU-based re-scoring, contribute to its effectiveness.
  • This advancement holds significant potential for improving autonomous marine cleanup operations and environmental monitoring.