Sonar Image Target Detection Based on Adaptive Global Feature Enhancement Network

Wang, Zhen; Zhang, Shanwen; Hang, Wenzhun; Guo, Jianxin; Zeng, Leya

doi:10.1109/jsen.2021.3131645

Cited by 34 publications

(9 citation statements)

References 66 publications

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“…The constructed dataset is named SSIS, and we provide pixel-level manual annotation information for each image. Most of the SSS images are collected from the public photo-sharing websites in the context of underwater search and rescue, and several SSS images are from the existing sonar image databased, such as QDdataset [47], AS-dataset [48], and SO-KLSG dataset [49]. The open source tool LabelMe [50] is used as sonar image annotation tool.…”

Section: A Benchmark Datasetmentioning

confidence: 99%

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Wang

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Side-scan sonar (SSS) is an essential acoustic sensor device for obtaining underwater information. The instance segmentation of sonar images can effectively locate and detect underwater objects. Although various CNN-based frameworks have achieved promising results in natural image instance segmentation, the noise interference, highlight shadow, and blurred edge in sonar images bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose a novel recurrent pyramid frequency feature fusion network (RPFNet), which mainly consists of the pyramid frequency feature fusion network (PFN), recurrent residual attention mechanism (RRAM), mask prediction module, and semantic segmentation module. By enhancing and fusing different frequency features of SSS images, PFN can effectively extract fine-grained features and reduce background information interference. The RRAM uses residual structure and attention mechanism to enhance the correlation of different frequency features and improve nonlinear feature representation ability. The mask prediction and semantic segmentation modules are used to discriminate object instance categories and generate corresponding semantic segmentation results. We extensively evaluate the proposed framework on the real-scenes sonar image dataset. The evaluation results demonstrate that the proposed method outperforms the state-ofthe-art methods in various evaluation metrics. Code is available at https://github.com/darkseid-arch/SonarRPFNet.

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Section: A Benchmark Datasetmentioning

confidence: 99%

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Wang

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This module, compatible with CNN models, incurred a modest parameter increase and displayed portability. The incorporation of shadow features improved detection accuracy (Wang et al, 2021). proposed AGFE-Net, a novel sonar image target detection algorithm.…”

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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“…Object-guided dual-adversarial contrast learning [25] and multi-scale fusion algorithm [26] can effectively enhance seriously distorted underwater images. Zhi Wang et al [27] proposed an adaptive global feature enhancement network (AGFE-Net) that used multi-scale convolution with global receptive fields and attention mechanisms to obtain multi-scale semantic features and enhance the correlation between features. Asymmetric non-local neural networks for semantic segmentation (ANNNet) [28] designed an asymmetric Non-Local approach to computing point-to-point similarity relationships efficiently and aggregating global information and context.…”

Section: Related Workmentioning

confidence: 99%

Global enhancement network underwater archaeology scene parsing method

Pan,

Jia,

Cai

2023

Robotica

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Underwater archaeology is of great significance for historical and cultural transmission and preservation of underwater heritage, but it is also a challenging task. Underwater heritage is located in an environment with high sediment content, objects are mostly buried, and the water is turbid, resulting in some of the features of objects missing or blurred, making it difficult to accurately identify and understand the semantics of various objects in the scene. To tackle these issues, this paper proposes a global enhancement network (GENet) underwater scene parsing method. We introduce adaptive dilated convolution by adding an extra regression layer, which can automatically deduce adaptive dilated coefficients according to the different scene objects. In addition, considering the easy confusion in the process of fuzzy feature classification, an enhancement classification network is proposed to increase the difference between various types of probabilities by reducing the loss function. We verified the validity of the proposed model by conducting numerous experiments on the Underwater Shipwreck Scenes (USS) dataset. We achieve state-of-the-art performance compared to the current state-of-the-art algorithm under three different conditions: conventional, relic semi-buried, and turbidified water quality. The experimental results show that the proposed algorithm performs best in different situations. To verify the generalizability of the proposed algorithm, we conducted comparative experiments on the current publicly available Cityscapes, ADE20K, and the underwater dataset SUIM. The experimental results show that this paper achieves good performance on the public dataset, indicating that the proposed algorithm is generalizable.

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Sonar Image Target Detection Based on Adaptive Global Feature Enhancement Network

Cited by 34 publications

References 66 publications

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Underwater small target detection based on dynamic convolution and attention mechanism

Global enhancement network underwater archaeology scene parsing method

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