An Improved U-Net Convolutional Networks for Seabed Mineral Image Segmentation

Song, Wei; Zheng, Nan; Liu, Xiangchun; Qiu, Lirong; Zheng, Rui

doi:10.1109/access.2019.2923753

Cited by 36 publications

(18 citation statements)

References 31 publications

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“…As for the underwater mineral image dataset, Reference [29] has proposed an improved U-Net, in the decoder part, the features are fused by different scale up-sampled operations to obtain the final segmentation map. For convenience, in this paper, the fusion structure is called the merge module and the network is named MU-Net.…”

Section: Related Workmentioning

confidence: 99%

Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

Wang¹,

Dong²,

Song³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Autonomous underwater vehicle (AUV) has many intelligent optical system, which can collect underwater signal information to make the system decision. One of them is the intelligent vision system, and it can capture the images to analyze. The performance of the particle image segmentation plays an important role in the monitoring of underwater mineral resources. In order to improve the underwater mineral image segmentation performance, some novel segmentation algorithm architectures are proposed. In this paper, an improved mineral image segmentation is proposed based on the modified U-Net. The pyramid upsampling module and residual module are bring into the U-Net model, which are called JPU-Net, JPMU-Net and ResU-Net. These models combined the power of the residual block and the pyramid upsampling in the encoder part and in the decoder part respectively. The proposed models are tested on the Electron Microscopy images (EM) dataset and the underwater mineral image dataset. The experimental results show that JPU-Net has superior performance on the EM dataset, and JPMU-Net has a better segmentation result than existing convolutional neural network on the underwater mineral image dataset.

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Section: Related Workmentioning

confidence: 99%

Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

Wang¹,

Dong²,

Song³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, U-Net [33] is well known and widely used in the field of medical image segmentation. Many works [34][35][36][37] in this field are based on U-Net. The holistically guided decoder proposed in EfficientFCN [38] leverages multi-level feature maps from the last three blocks of the encoder to achieve highlevel feature up-sampling with semantic-rich features.…”

Section: Semantic Segmentationmentioning

confidence: 99%

DPANet: Dual Pooling‐aggregated Attention Network for fish segmentation

Zhang

Bao

2021

IET Computer Vision

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The sustainable development of marine fisheries depends on the accurate measurement of data on fish stocks. Semantic segmentation methods based on deep learning can be applied to automatically obtain segmentation masks of fish in images to obtain measurement data. However, general semantic segmentation methods cannot accurately segment fish objects in underwater images. In this study, a Dual Pooling-aggregated Attention Network (DPANet) to adaptively capture long-range dependencies through an efficient and computing-friendly manner to enhance feature representation and improve segmentation performance is proposed. Specifically, a novel pooling-aggregate position attention module and a poolingaggregate channel attention module are designed to aggregate contexts in the spatial dimension and channel dimension, respectively. These two modules adopt pooling operations along the channel dimension and along the spatial dimension to aggregate information, respectively, thus reducing computational costs. In these modules, attention maps are generated by four different paths and are aggregated into one. The authors conduct extensive experiments to validate the effectiveness of the DPANet and achieve new state-ofthe-art segmentation performance on the well-known fish image dataset DeepFish as well as on the underwater image dataset SUIM, achieving a Mean IoU score of 91.08% and 85.39% respectively, while significantly reducing FLOPs of attention modules by about 93%.

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“…In recent years, we have witnessed great progress for deep convolutional neural networks [23], [24] based image segmentation and various methods have been proposed. Deep learning has been applied to solve the problems related to fully supervised semantic segmentation [13], [14], [16], [17], [25]- [27], interactive segmentation [28]- [30] and unsupervised segmentation [18], [31], [32]. Besides the fully supervised semantic segmentation, deep learning has proven to be effective in solving the interactive segmentation problem as well.…”

Section: B Deep Learning and Image Segmentationmentioning

confidence: 99%

DIC: Deep Image Clustering for Unsupervised Image Segmentation

Zhou

Wei

2020

IEEE Access

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Unsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are sensitive to the parameters such as the segmentation numbers or of high training and inference complexity. Encouraged by neural networks' flexibility and their ability for modelling intricate patterns, an unsupervised segmentation framework based on a novel deep image clustering (DIC) model is proposed. The DIC consists of a feature transformation subnetwork (FTS) and a trainable deep clustering subnetwork (DCS) for unsupervised image clustering. FTS is built on a simple and capable network architecture. DCS can assign pixels with different cluster numbers by updating cluster associations and cluster centers iteratively. Moreover, a superpixel guided iterative refinement loss is designed to optimize the DIC parameters in an overfitting manner. Extensive experiments have been conducted on the Berkley Segmentation Database. The experimental results show that DCS is more effective in aggregating features during the clustering procedure. DIC has also proven to be less sensitive to varying segmentation parameters and of lower computation costs, and DIC can achieve significantly better segmentation performance compared to the state-of-the-art techniques. The source code is available on https://github.com/zmbhou/DIC.

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An Improved U-Net Convolutional Networks for Seabed Mineral Image Segmentation

Cited by 36 publications

References 31 publications

Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

DPANet: Dual Pooling‐aggregated Attention Network for fish segmentation

DIC: Deep Image Clustering for Unsupervised Image Segmentation

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