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Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Underwater Target Detection Based on Parallel High-Resolution Networks.

Zhengwei Bao^1,2, Ying Guo^1,2, Jiyu Wang^1,2

¹College of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China.

Sensors (Basel, Switzerland)

|September 9, 2023

Summary

This study introduces a novel high-resolution network for underwater target detection, enhancing feature extraction in complex scenes. The improved network demonstrates superior performance on multiple datasets, advancing underwater object recognition capabilities.

Keywords:

attention mechanism parallel high-resolution networks receptive field augmentation target detection underwater

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Area of Science:

Computer Vision
Robotics
Image Processing

Background:

Underwater environments present complex scenes challenging for target detection.
Limited target feature extraction capability hinders accurate identification of underwater objects.
Existing methods often struggle with small, fuzzy, or irregularly shaped targets.

Purpose of the Study:

To propose a parallel high-resolution underwater target detection network.
To enhance feature representation and reduce semantic information loss during sampling.
To improve the detection of multi-scale underwater targets in complex scenes.

Main Methods:

Utilized a high-resolution network (HRNet) for improved target feature representation.
Introduced an improved attention module (A-CBAM) with flexible rectified linear units (FReLU) for pixel-level spatial modeling.
Incorporated a receptive field augmentation module (RFAM) for enhanced feature robustness and discrimination.

Main Results:

Achieved 81.17% mAP on URPC2020.
Obtained 77.02% mAP on URPC2018.
Reached 82.9% mAP on PASCAL VOC2007.

Conclusions:

The proposed network effectively addresses challenges in complex underwater scenes.
The integration of HRNet, A-CBAM with FReLU, and RFAM significantly improves underwater target detection.
Experimental results validate the network's effectiveness and robustness for multi-scale underwater object recognition.