Automatic Detection of Underwater Small Targets Using Forward-Looking Sonar Images

Zhou, Tian; Si, Jikun; Wang, Luyao; Xu, Chao; Yu, Xiaoyang

doi:10.1109/tgrs.2022.3181417

Cited by 34 publications

(15 citation statements)

References 47 publications

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“…Interestingly, the meteorological conditions in the same geographical area may also be influential. In this regard, Jerlov water types are a commonly used means of underwater classification in the literature, which considers radiation absorption and scattering properties to categorize the spectral behavior of water [ 35 ]. In this paper, five Jerlov water models are considered for both open ocean and coastal water types.…”

Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals. This issue has been stated in the literature as a limiting factor to be addressed in the future. The key starting point of this work is to identify a synergistic effect between professional photography and scientific fields by analyzing image acquisition issues. Subsequently, we discuss underwater image enhancement and quality assessment, image mosaicking and algorithmic concerns as the last processing step. In this line, statistics about 120 AUV articles fro recent decades have been analyzed, with a special focus on state-of-the-art papers from recent years. Therefore, the aim of this paper is to identify critical issues in autonomous underwater vehicles encompassing the entire process, starting from optical issues in image sensing and ending with some issues related to algorithmic processing. In addition, a global underwater workflow is proposed, extracting future requirements, outcome effects and new perspectives in this context.

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Section: Critical Issues In Auv Underwater Imagingmentioning

confidence: 99%

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Monterroso Muñoz,

Moron-Fernández,

Cascado-Caballero

et al. 2023

Sensors

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show abstract

“…The resolution of an FLS image is affected by acoustic frequency, beam width, signal processing method, etc. It is suitable for underwater navigation and for avoiding obstacles [3,38,42,43]. SAS receives and synthesizes the amplitude and phase information of a series of echo signals through a receiver traveling a relatively short distance and generates high-resolution images with an increase in the imaging aperture, making it suitable for remote detection tasks [44][45][46][47].…”

Section: Oa (%)mentioning

confidence: 99%

An Underwater Side-Scan Sonar Transfer Recognition Method Based on Crossed Point-to-Point Second-Order Self-Attention Mechanism

Wang,

Li,

Dong

et al. 2023

Remote Sensing

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Recognizing targets through side-scan sonar (SSS) data by deep learning-based techniques has been particularly challenging. The primary challenge stems from the difficulty and time consumption associated with underwater acoustic data acquisition, which demands systematic explorations to obtain sufficient training samples for accurate deep learning-based models. Moreover, if the sample size of the available data is small, the design of effective target recognition models becomes complex. These challenges have posed significant obstacles to developing accurate SSS-based target recognition methods via deep learning models. However, utilizing multi-modal datasets to enhance the recognition performance of sonar images through knowledge transfer in deep networks appears promising. Owing to the unique statistical properties of various modal images, transitioning between different modalities can significantly increase the complexity of network training. This issue remains unresolved, directly impacting the target transfer recognition performance. To enhance the precision of categorizing underwater sonar images when faced with a limited number of mode types and data samples, this study introduces a crossed point-to-point second-order self-attention (PPCSSA) method based on double-mode sample transfer recognition. In the PPCSSA method, first-order importance features are derived by extracting key horizontal and longitudinal point-to-point features. Based on these features, the self-supervised attention strategy effectively removes redundant features, securing the second-order significant features of SSS images. This strategy introduces a potent low-mode-type small-sample learning method for transfer learning. Classification experiment results indicate that the proposed method excels in extracting key features with minimal training complexity. Moreover, experimental outcomes underscore that the proposed technique enhances recognition stability and accuracy, achieving a remarkable overall accuracy rate of 99.28%. Finally, the proposed method maintains high recognition accuracy even in noisy environments.

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“…Especially, Zhou et al. [25] present an automatic underwater target detection method for small target detection in forward‐looking sonar images. Compared with the commonly used deep learning detection models, the method achieves better results in detecting small targets.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental results show that the proposed algorithm improves obstacles' detection accuracy and processing speed in forward-looking sonar. Especially, Zhou et al [25] present an automatic underwater target detection method for small target detection in forwardlooking sonar images. Compared with the commonly used deep learning detection models, the method achieves better results in detecting small targets.…”

Section: Detection For Sonar Imagesmentioning

confidence: 99%

SDNet: Image‐based sonar detection network for multi‐scale objects

Shi

Sun

Xin

et al. 2022

IET Image Processing

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Autonomous Underwater Vehicle (AUV) carrying sonar for object detection has become one of the main ways of ocean exploration. However, object detection in sonar images always faces the problems of balancing detection accuracy and efficiency. To this end, this paper proposes an efficient underwater object detection network for sonar images named SDNet. In the model, the authors construct a new feature extraction network based on RepVGG to balance the detection accuracy and speed. By combining channel attention with RepVGG, useful information of high-order feature maps captured can be selectively paid attention to. Then, the authors design a feature fusion network to efficiently converge the location and semantic information of multi-scale feature maps. In the network, the authors propose a lightweight cross stage partial network for sonar (CSP_S) module suitable for sonar images, which can enhance the model's feature fusion capability and simplify the model. Finally, to reduce the conflict between classification and regression tasks, the authors leverage the Decoupled Head for the sonar object classification and localization. By testing on the self-built Underwater Sonar Dataset underwater sonar dataset (USD) and the public sonar dataset sonar common target detection dataset (SCTD), the detection accuracy of SDNet reaches 99.52% and 95.20%, respectively. Moreover, the detection speed reaches 114 frames per second (FPS) and 138 FPS, respectively. The experimental results show that SDNet can effectively balance the sonar detection accuracy and efficiency.

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Automatic Detection of Underwater Small Targets Using Forward-Looking Sonar Images

Cited by 34 publications

References 47 publications

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition

An Underwater Side-Scan Sonar Transfer Recognition Method Based on Crossed Point-to-Point Second-Order Self-Attention Mechanism

SDNet: Image‐based sonar detection network for multi‐scale objects

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