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Enhanced Atrous Spatial Pyramid Pooling Feature Fusion for Small Ship Instance Segmentation.

Rabi Sharma¹, Muhammad Saqib^1,2, C T Lin¹

¹School of Computer Science, University of Technology Sydney, Broadway, Sydney 2007, Australia.

Journal of Imaging

|December 27, 2024

Summary

This summary is machine-generated.

This study introduces an enhanced Atrous Spatial Pyramid Pooling (ASPP) feature fusion method to improve the instance segmentation of small ships in maritime settings. The novel approach significantly boosts accuracy where other algorithms fail.

Keywords:

attention convolutional neural network instance segmentation maritime surveillance

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Area of Science:

Computer Vision
Maritime Surveillance
Deep Learning

Background:

Accurate instance segmentation of small ships in maritime environments is critical for applications like navigation safety and security.
Existing algorithms struggle to detect and segment small ships due to their limited appearance, small size, and distant locations.

Purpose of the Study:

To develop a novel method for enhancing the instance segmentation of small ships.
To address the limitations of current algorithms in detecting small maritime objects.

Main Methods:

Proposed an enhanced Atrous Spatial Pyramid Pooling (ASPP) feature fusion module specifically designed to refine and fuse features of small objects.
Integrated the enhanced ASPP module into an instance segmentation framework.

Main Results:

The proposed framework demonstrated superior performance compared to state-of-the-art models like Mask R-CNN and SOLOv2.
Achieved high average precision (mask AP) scores: 75.8% on ShipSG, 69.5% on ShipInsSeg, and 54.5% on MariBoats datasets.

Conclusions:

The enhanced ASPP feature fusion method effectively improves small ship instance segmentation in maritime scenes.
The developed framework offers a robust solution for accurate detection and segmentation of small vessels, outperforming existing methods.