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

Xidian University, China Xidian University, China Jiangxi University of Science and Technology, China Institute of Deep-sea Science and Engineering, China 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. I NTRODUCTION Underwater sonar imaging plays an indispensable role in marine exploration and various ocean industries, providing valuable insights into underwater environments. Unlike optical imaging, where light propagation is restricted, sonar systems utilize sound waves that travel farther, allowing them to cover larger underwater areas. This makes sonar images an ideal choice for applications such as seabed mapping, underwater object detection, and navigation. However, despite the advantages of sonar imaging, its image quality is often severely compromised by noise, which negatively impacts the accuracy of downstream tasks, such as object detection. In sonar images, noise can originate from various factors, including environmental interference, sensor imperfections, and the inherent characteristics of sound wave propagation Corresponding authors: Tao Xue, Y anbin Wang. in water. Common types of sonar image noise include Gaussian noise, speckle noise, and Poisson noise. Gaussian noise typically arises from random fluctuations in sensor readings or environmental changes. Speckle noise, caused by sound wave scattering, manifests as granular interference, which can obscure object boundaries.