AUV-Based Side-Scan Sonar Real-Time Method for Underwater-Target Detection

Tang, Yulin; Wei, Liming; Jin, Shuanggen; Zhao, Jianhu; Huang, Chao; Yu, Yongcan

doi:10.3390/jmse11040690

Cited by 23 publications

(15 citation statements)

References 37 publications

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“…Chu [10] utilized deep reinforcement learning based on a double-deep Q-network for autonomous underwater navigation, leading to effective path planning and obstacle avoidance. Tang [11] developed a deep learning-based underwater target detection model and a real-time underwater target detection method, effectively addressing the challenges associated with side-scan sonar recognition. Refraction of light caused the loss of image features, making it more difficult for the algorithm to accurately identify the target.…”

Section: Related Workmentioning

confidence: 99%

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Cai,

Zhang,

et al. 2024

Robotica

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The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.

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Section: Related Workmentioning

confidence: 99%

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Cai,

Zhang,

et al. 2024

Robotica

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show abstract

“…The confidence loss function was also improved to bias the network towards learning high-quality positive anchor boxes to enhance the network’s ability to detect objects. Tang et al [ 36 ] proposed a hybrid DETR-YOLO detection model, which utilizes the DETR module for global feature extraction of the input and combines the lightweight advantages of YOLO to improve the accuracy of small object detection. However, the model is mainly applied to side-scan sonar and cannot be directly migrated to underwater optical images.…”

Section: Related Workmentioning

confidence: 99%

Underwater object detection method based on learnable query recall mechanism and lightweight adapter

Lin,

Huang,

Wang

2024

PLoS ONE

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With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.

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“…Extensive research has been undertaken in the domain of underwater acoustic image target detection (Lee et al, 2018;Zhang et al, 2021b;Kim et al, 2022;Tang et al, 2023). These endeavors encompass the design of specialized functional modules tailored to data characteristics or the adaptation and enhancement of networks originally well-suited for optical data to underwater acoustic data.…”

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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AUV-Based Side-Scan Sonar Real-Time Method for Underwater-Target Detection

Cited by 23 publications

References 37 publications

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles

Underwater object detection method based on learnable query recall mechanism and lightweight adapter

Underwater small target detection based on dynamic convolution and attention mechanism

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