Underwater target detection with an attention mechanism and improved scale

Wei, Xiangyu; Yu, Long; Tian, Shengwei; Feng, Pengcheng; Ning, Xin

doi:10.1007/s11042-021-11230-2

Cited by 48 publications

(20 citation statements)

References 20 publications

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“…When the computing power is limited, the attention mechanism in neural network allocates computing resources to more important tasks. In recent years, attention mechanism has been widely used in deep neural networks [10,24,25,31,34]. Hu et al [9] proposed SENet to learn the correlation between channels, and achieved significant performance improvement in image classification.…”

Section: Attention Mechanismmentioning

confidence: 99%

Super resolution reconstruction of CT images based on multi-scale attention mechanism

Yin

et al. 2023

Multimed Tools Appl

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CT diagnosis has been widely used in clinic because of its special diagnostic value. The image resolution of CT imaging system is constrained by X-ray focus size, detector element spacing, reconstruction algorithm and other factors, which makes the generated CT image have some problems, such as low contrast, insufficient high-frequency information, poor perceptual quality and so on. To solve the above problems, a super-resolution reconstruction method of CT image based on multi-scale attention mechanism is proposed. First, use a 3 × 3 and a 1 × 1 convolution layer extracting shallow features. In order to better extract the high-frequency features of CT images and improve the image contrast, a multi-scale attention module is designed to adaptively detect the information of different scales, improve the expression ability of features, integrate the channel attention mechanism and spatial attention mechanism, and pay more attention to important information, retain more valuable information. Finally, sub-pixel convolution is used to improve the resolution of CT image and reconstruct high-resolution CT image. The experimental results show that this method can effectively improve the CT image contrast and suppress the noise. The peak signal-to-noise ratio and structural similarity of the reconstructed CT image are better than the comparison method, and has a good subjective visual effect.

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Section: Attention Mechanismmentioning

confidence: 99%

Super resolution reconstruction of CT images based on multi-scale attention mechanism

Yin

et al. 2023

Multimed Tools Appl

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“…Chen et al [35] proposed a novel sample-weighted hyper-network to address the blurring of underwater images under severe noise interference. Wei et al [36] built a generalized model to address the complex environment in underwater object detection by simulating data augmentation strategies for overlapping, occluded, and blurred objects.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Underwater Object Detection Network Using Swin Transformer

Liu

et al. 2022

IEEE Access

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Underwater object detection plays an essential role in ocean exploration, and the increasing amount of underwater object image data makes the study of advanced underwater object detection algorithms of great practical significance. However, there are problems with colour offset, low contrast, and target blur in underwater image data. An underwater object detection algorithm based on Faster R-CNN is proposed to solve these problems. First, the Swin Transformer is used as the backbone network of the algorithm. Second, by adding the path aggregation network, the deep feature map and the shallow feature map are superimposed and fused. Third, online hard example mining, makes the training process more efficient. Fourth, the ROI pooling is improved to ROI align, eliminating the two quantization errors of ROI pooling and improving the detection performance. Compared with other algorithms, the proposed algorithm's based on improved Faster-RCNN on URPC2018 dataset is improved to 80.54%, and basically solve the problem of missed detection and false detection of objects of different sizes in a complex environment.

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“…are more complex than the surface environment (Qiang et al, 2020) (Lei et al, 2022). Due to the differential attenuation of different wavelengths of light in water, scattering of light by plankton and suspended particles in water (Wei et al, 2021), making the target in underwater images and videos blurred and with severe color cast, it seriously affects the features of the target and creates serious obstacles for feature learning and recognition understanding of underwater targets. Therefore, underwater target detection continues to face a huge challenge (Jiang and Wang, 2020).…”

Section: Introductionmentioning

confidence: 99%

Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling

et al. 2022

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For the routine target detection algorithm in the underwater complex environment to obtain the image of the existence of blurred images, complex background and other phenomena, leading to difficulties in model feature extraction, target miss detection and other problems. Meanwhile, an improved YOLOv7 model is proposed in order to improve the accuracy and real-time performance of the underwater target detection model. The improved model is based on the single-stage target detection model YOLOv7, incorporating the CBAM attention mechanism in the model, so that the feature information of the detection target is weighted and enhanced in the spatial dimension and the channel dimension, capturing the local relevance of feature information, making the model more focused on target feature information, improved detection accuracy, and using the SPPFCSPC module, reducing the computational effort of the model while keeping the model perceptual field unchanged, improved inference speed of the model. After a large number of comparison experiments and ablation experiments, it is proved that our proposed ACFP-YOLO algorithm model has higher detection accuracy compared with Efficientdet, Faster-RCNN, SSD, YOLOv3, YOLOv4, YOLOv5 models and the latest YOLOv7 model, and is more accurate for target detection tasks in complex underwater environments advantages.

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Underwater target detection with an attention mechanism and improved scale

Cited by 48 publications

References 20 publications

Super resolution reconstruction of CT images based on multi-scale attention mechanism

Super resolution reconstruction of CT images based on multi-scale attention mechanism

Two-Stage Underwater Object Detection Network Using Swin Transformer

Underwater object detection algorithm based on attention mechanism and cross-stage partial fast spatial pyramidal pooling

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