Semantic attention and relative scene depth-guided network for underwater image enhancement

Chen, Tingkai; Wang, Weining; Chen, Yanzheng; Kong, Xiangjun; Lin, Yejin; Hong, Zhao; Karimi, Hamid Reza

doi:10.1016/j.engappai.2023.106532

Cited by 13 publications

(1 citation statement)

References 65 publications

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“…Significant achievements have been made in each of these fields. Moreover, CNN-based methods can also be employed for image enhancement to improve the quality of blurry images and enhance the recognition of regions of interest (Chen et al, 2023;Wang et al, 2023b), thereby enhancing the effectiveness of target detection. Therefore, investigating how to apply CNN-based object detection methods more efficiently to the field of underwater acoustic image target detection is a highly worthwhile research endeavor.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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UP‐GAN: Channel‐spatial attention‐based progressive generative adversarial network for underwater image enhancement

Wang,

Chen,

Wei

et al. 2024

Journal of Field Robotics

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Focusing on severe color deviation, low brightness, and mixed noise caused by inherent scattering and light attenuation effects within underwater environments, an underwater‐attention progressive generative adversarial network (UP‐GAN) is innovated for underwater image enhancement (UIE). Salient contributions are as follows: (1) By elaborately devising an underwater background light estimation module via an underwater imaging model, the degradation mechanism can be sufficiently integrated to fuse prior information, which in turn saves computational burden on subsequent enhancement; (2) to suppress mixed noise and enhance foreground, simultaneously, an underwater dual‐attention module is created to fertilize skip connection from channel and spatial aspects, thereby getting rid of noise amplification within the UIE; and (3) by systematically combining with spatial consistency, exposure control, color constancy, color relative dispersion losses, the entire UP‐GAN framework is skillfully optimized by taking into account multidegradation factors. Comprehensive experiments conducted on the UIEB data set demonstrate the effectiveness and superiority of the proposed UP‐GAN in terms of both subjective and objective aspects.

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Open-set marine object instance segmentation with prototype learning

Hu,

Li,

Karimi

et al. 2024

SIViP

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The ocean world is full of Unknown Marine Objects (UMOs), making it difficult to deal with unknown ocean targets using the traditional instance segmentation model. This is because the traditional instance segmentation networks are trained on a closed dataset, assuming that all detected objects are Known Marine Objects (KMOs). Consequently, traditional closed-set networks often misclassify UMOs as KMOs. To address this problem, this paper proposes a new open-set instance segmentation model for object instance segmentation in marine environments with UMOs. Specifically, we integrate two learning modules in the model, namely a prototype module and an unknown learning module. Through the learnable prototype, the prototype module improves the class’s compactness and boundary detection capabilities while also increasing the classification accuracy. Through the uncertainty of low probability samples, the unknown learning module forecasts the unknown probability. Experimental results illustrate that the proposed method has competitive known class recognition accuracy compared to existing instance segmentation models, and can accurately distinguish unknown targets.

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Semantic attention and relative scene depth-guided network for underwater image enhancement

Cited by 13 publications

References 65 publications

Underwater small target detection based on dynamic convolution and attention mechanism

Underwater small target detection based on dynamic convolution and attention mechanism

UP‐GAN: Channel‐spatial attention‐based progressive generative adversarial network for underwater image enhancement

Open-set marine object instance segmentation with prototype learning

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