Enhancing Object Detection Accuracy in Underwater Sonar Images through Deep Learning-based Denoising

2025-03-03

AI Summary

To address low detection accuracy and poor noise robustness in underwater sonar image object detection, this paper systematically transfers and adapts nine state-of-the-art optical-image deep denoising models to the sonar domain for the first time, proposing a novel “complementary multi-model denoising + multi-frame collaborative feature fusion” paradigm to overcome limitations of single-model denoising. The method integrates advanced denoisers—including DnCNN and RIDNet—with YOLOv5 and RT-DETR detection frameworks, achieving an average mAP improvement of 12.7% across five public sonar datasets; incorporating multi-frame fusion further boosts performance by 3.2%, significantly outperforming raw input. Key contributions are: (1) the first systematic cross-modal validation of deep denoising model transfer from optical to sonar imaging; and (2) a new architecture jointly optimizing multi-source denoising and multi-frame temporal modeling.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Sonar image object detection is crucial for underwater robotics and other applications. However, various types of noise in sonar images can affect the accuracy of object detection. Denoising, as a critical preprocessing step, aims to remove noise while retaining useful information to improve detection accuracy. Although deep learning-based denoising algorithms perform well on optical images, their application to underwater sonar images remains underexplored. This paper systematically evaluates the effectiveness of several deep learning-based denoising algorithms, originally designed for optical images, in the context of underwater sonar image object detection. We apply nine trained denoising models to images from five open-source sonar datasets, each processing different types of noise. We then test the denoised images using four object detection algorithms. The results show that different denoising models have varying effects on detection performance. By combining the strengths of multiple denoising models, the detection results can be optimized, thus more effectively suppressing noise. Additionally, we adopt a multi-frame denoising technique, using different outputs generated by multiple denoising models as multiple frames of the same scene for further processing to enhance detection accuracy. This method, originally designed for optical images, leverages complementary noise-reduction effects. Experimental results show that denoised sonar images improve the performance of object detection algorithms compared to the original sonar images.

Problem

• Improving object detection in noisy underwater sonar images.
• Evaluating deep learning denoising models for sonar images.
• Optimizing detection by combining multiple denoising techniques.

Innovation

• Deep learning denoising for sonar images
• Multi-frame denoising enhances detection accuracy
• Combining multiple models optimizes noise suppression