A Texture Feature Removal Network for Sonar Image Classification and Detection

Li, Chuanlong; Ye, Xiufen; Ji'er, Xi; Jia, Yunpeng

doi:10.3390/rs15030616

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…Products from Norway Kongsberg and Canada PanGeo are relatively classic, as shown in figure 9. Among them, PanGeo company named the SBI has been commercialized and applied in the field of offshore engineering [60]. Its key advantage is the use of a phased receive array with a large aperture and low frequency.…”

Section: Side Scan Sonarmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as SIFT (Scale-Invariant Feature Transform), KMA (K-means algorithm), and CFAR (Constant False-Alarm Rate) that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

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Section: Side Scan Sonarmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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“…Employing a bidirectional feature pyramid network and an adaptive feature fusion block enabled the acquisition of deep semantic features, suppression of background noise interference, and precise prediction box selection through an adaptive nonmaximum suppression algorithm, ultimately enhancing target localization accuracy. To address the issue of suboptimal transfer learning results due to significant domain gaps between optical and sonar images (Li et al, 2023a), introduced a transfer learning method for sonar image classification and object detection known as the Texture Feature Removal Network. They considered texture features in images as domain-specific features and mitigated domain gaps by discarding these domain-specific features, facilitating a more seamless knowledge transfer process.…”

Section: Related Workmentioning

confidence: 99%

Underwater small target detection based on dynamic convolution and attention mechanism

Cheng,

Wang,

Yang

et al. 2024

Front. Mar. Sci.

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In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

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“…Their approach achieved a precision of 85.3% and a recall of 94.5%. Li et al [6] identified texture features as domain-specific features and proposed to narrow the domain gap by removing these features. This method successfully transferred knowledge from optical images to sonar image classification tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with the limitations of optical sensors in detecting targets, such as short detection distances and poor underwater visibility, SSS-based target detection methods have become increasingly popular and effective. These methods [4][5][6] have proven to be more effective in terms of distance and visibility, overcoming the limitations of traditional optical sensors. The lack of sonar sample data and image quality remains a common problem in sonar target recognition.…”

Section: Introductionmentioning

confidence: 99%

Sonar Image Target Detection Based on Simulated Stain-like Noise and Shadow Enhancement in Optical Images under Zero-Shot Learning

Xi,

2024

JMSE

Self Cite

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There are many challenges in using side-scan sonar (SSS) images to detect objects. The challenge of object detection and recognition in sonar data is greater than in optical images due to the sparsity of detectable targets. The complexity of real-world underwater scanning presents additional difficulties, as different angles produce sonar images of varying characteristics. This heterogeneity makes it difficult for algorithms to accurately identify and detect sonar objects. To solve these problems, this paper presents a novel method for sonar image target detection based on a transformer and YOLOv7. Thus, two data augmentation techniques are introduced to improve the performance of the detection system. The first technique applies stain-like noise to the training optical image data to simulate the real sonar image environment. The second technique adds multiple shadows to the optical image and 3D data targets to represent the direction of the target in the sonar image. The proposed method is evaluated on a public sonar image dataset, and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed. The experimental results show that our method achieves better precision.

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A Texture Feature Removal Network for Sonar Image Classification and Detection

Cited by 12 publications

References 40 publications

Sonar image intelligent processing in seabed pipeline detection: review and application

Sonar image intelligent processing in seabed pipeline detection: review and application

Underwater small target detection based on dynamic convolution and attention mechanism

Sonar Image Target Detection Based on Simulated Stain-like Noise and Shadow Enhancement in Optical Images under Zero-Shot Learning

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