Underwater Target Detection Based on Parallel High-Resolution Networks

Bao, Zhengwei; Guo, Ying; Wang, Jiyu; Zhu, Linlin; Huang, Jun; Yan, Shu

doi:10.3390/s23177337

Cited by 7 publications

(1 citation statement)

References 44 publications

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“…To improve real-time and lightweight performance, they introduced a lightweight underwater object detection method that incorporates MobileNet v2, the YOLOv4 algorithm, and an attention mechanism. In 2023, to address the challenges posed by the intricate underwater scenes and the limited ability to extract object features, Zhengwei Bao et al [34] proposed a parallel high-resolution network for underwater object detection. Kaiyue Liu et al [35] enhanced the model performance by incorporating a residual module and integrating a global attentional mechanism into the object detection network.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Underwater Object Detection Algorithm for Embedded Deployment Using Higher-Order Information and Image Enhancement

Liu,

Wen,

Huang

et al. 2024

JMSE

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Underwater object detection is crucial in marine exploration, presenting a challenging problem in computer vision due to factors like light attenuation, scattering, and background interference. Existing underwater object detection models face challenges such as low robustness, extensive computation of model parameters, and a high false detection rate. To address these challenges, this paper proposes a lightweight underwater object detection method integrating deep learning and image enhancement. Firstly, FUnIE-GAN is employed to perform data enhancement to restore the authentic colors of underwater images, and subsequently, the restored images are fed into an enhanced object detection network named YOLOv7-GN proposed in this paper. Secondly, a lightweight higher-order attention layer aggregation network (ACC3-ELAN) is designed to improve the fusion perception of higher-order features in the backbone network. Moreover, the head network is enhanced by leveraging the interaction of multi-scale higher-order information, additionally fusing higher-order semantic information from features at different scales. To further streamline the entire network, we also introduce the AC-ELAN-t module, which is derived from pruning based on ACC3-ELAN. Finally, the algorithm undergoes practical testing on a biomimetic sea flatworm underwater robot. The experimental results on the DUO dataset show that our proposed method improves the performance of object detection in underwater environments. It provides a valuable reference for realizing object detection in underwater embedded devices with great practical potential.

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Section: Introductionmentioning

confidence: 99%

Lightweight Underwater Object Detection Algorithm for Embedded Deployment Using Higher-Order Information and Image Enhancement

Liu,

Wen,

Huang

et al. 2024

JMSE

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Lightweight enhanced YOLOv8n underwater object detection network for low light environments

Ding,

Hu,

Lin

et al. 2024

Sci Rep

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In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model’s perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection.

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One stage multi-scale efficient network for underwater target detection

Zhang,

Dai,

Chen

et al. 2024

Review of Scientific Instruments

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Due to the complexity of the underwater environment, existing methods for underwater target detection present low precision on small or dense targets. To address these issues, a novel method is proposed for underwater target detection based on YOLOv5s (You Only Look Once version 5 small), which aims to improve the precision and robustness. In this study, an efficient feature extraction network is introduced to extract significant features, and a novel attention mechanism with deformable convolution is designed to improve the feature representation. Subsequently, an adaptive spatial fusion operation is introduced at the neck of YOLOv5s to facilitate feature fusion from various layers. By integrating low-level features with high-level features, the adaptive fusion feature pyramid network effectively integrates global semantic information and decreases the semantic gap between features from various layers, contributing to the high detection precision. Comprehensive experiments demonstrate that the proposed method achieves an mAP50 of 86.97% on the Underwater Robot Professional Contest of China 2020 dataset, 3.07% higher than YOLOv5s. Furthermore, the proposed method achieves a detection precision of 76.0% on the PASCAL VOC2007 dataset, surpassing several outstanding methods.

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Underwater Target Detection Based on Parallel High-Resolution Networks

Cited by 7 publications

References 44 publications

Lightweight Underwater Object Detection Algorithm for Embedded Deployment Using Higher-Order Information and Image Enhancement

Lightweight Underwater Object Detection Algorithm for Embedded Deployment Using Higher-Order Information and Image Enhancement

Lightweight enhanced YOLOv8n underwater object detection network for low light environments

One stage multi-scale efficient network for underwater target detection

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