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Related Experiment Video

Updated: May 6, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Published on: March 6, 2014

Underwater object detection method based on learnable query recall mechanism and lightweight adapter.

Xi Lin¹, Xixia Huang¹, Le Wang¹

¹Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.

|February 28, 2024

Summary

This study enhances underwater object detection by improving DETR with a query recall mechanism and multi-scale features, significantly boosting performance in challenging marine environments.

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Area of Science:

Marine science
Computer vision
Oceanography

Background:

Underwater object detection is crucial for aquaculture, environmental monitoring, and marine science.
Existing deep learning models struggle with underwater image challenges like noise, blur, and multi-scale objects.

Purpose of the Study:

To adapt the DETR (DEtection TRansformer) model for improved underwater object detection.
To address limitations in current algorithms for detecting small, irregular, and noisy underwater targets.

Main Methods:

Introduced a learnable query recall mechanism to reduce noise impact.
Developed a lightweight adapter for multi-scale feature extraction in encoding and decoding.
Optimized bounding box regression using a combination of smooth L1 and CIoU loss.

Main Results:

The proposed method demonstrated significant improvements in underwater object detection.
Validated effectiveness against state-of-the-art methods on the RUOD dataset.
Achieved superior performance in handling noise, blur, and multi-scale objects.

Conclusions:

The enhanced DETR model effectively addresses challenges in underwater object detection.
The proposed modifications offer a robust solution for marine visual tasks.
This work advances the capabilities of deep learning in underwater imaging applications.