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Rethinking 1D convolution for lightweight semantic segmentation

FRONTIERS IN NEUROROBOTICS 2023年 17卷 1119231页

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Xu, Chenglong Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

Lightweight semantic segmentation promotes the application of semantic segmentation in tiny devices. The existing lightweight semantic segmentation network (LSNet) has the problems of low precision and a large number of parameters. In response to the above problems, we designed a full 1D convolutional LSNet. The tremendous success of this network is attributed to the following three modules: 1D multi-layer space module (1D-MS), 1D multi-layer channel module (1D-MC), and flow alignment module (FA). The 1D-MS and the 1D-MC add global feature extraction operations based on the multi-layer perceptron (MLP) idea. This module uses 1D convolutional coding, which is more flexible than MLP. It increases the global information operation, improving features' coding ability. The FA module fuses high-level and low-level semantic information, which solves the problem of precision loss caused by the misalignment of features. We designed a 1D-mixer encoder based on the transformer structure. It performed fusion encoding of the feature space information extracted by the 1D-MS module and the channel information extracted by the 1D-MC module. 1D-mixer obtains high-quality encoded features with very few parameters, which is the key to the network's success. The attention pyramid with FA (AP-FA) uses an AP to decode features and adds a FA module to solve the problem of feature misalignment. Our network requires no pre-training and only needs a 1080Ti GPU for training. It achieved 72.6 mIoU and 95.6 FPS on the Cityscapes dataset and 70.5 mIoU and 122 FPS on the CamVid dataset. We ported the network trained on the ADE2K dataset to mobile devices, and the latency of 224 ms proves the application value of the network on mobile devices. The results on the three datasets prove that the network generalization ability we designed is powerful. Compared to state-of-the-art lightweight semantic segmentation algorithms, our designed network achieves the best balance between segmentation accuracy an

关键词： semantic segmentation lightweight network 1D convolution encoder-decoder feature alignment

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ERV-Net: An efficient 3D residual neural network for brain tumor segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 170卷 114566-114566页

作者： Zhou, Xinyu Li, Xuanya Hu, Kai Zhang, Yuan Chen, Zhineng Gao, Xieping Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan 411105 Peoples R China Baidu Inc Beijing 100085 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Xiangnan Univ Coll Med Imaging & Inspect Chenzhou Peoples R China

Brain tumors are the most aggressive and mortal cancers, which lead to short life expectancy. A reliable and efficient automatic or semi-automatic segmentation method is significant for clinical practice. In recent years, deep learning-based methods achieve great success in brain tumor segmentation. However, due to the limitation of parameters and computational complexity, there is still much room for improvement in these methods. In this paper, we propose an efficient 3D residual neural network (ERV-Net) for brain tumor segmentation, which has less computational complexity and GPU memory consumption. In ERV-Net, a computation-efficient network, 3D ShuffleNetV2, is firstly utilized as encoder to reduce GPU memory and improve the efficiency of ERV-Net, and then the decoder with residual blocks (Res-decoder) is introduced to avoid degradation. Furthermore, a fusion loss function, which is composed of Dice loss and Cross-entropy loss, is developed to solve the problems of network convergence and data imbalance. Moreover, a concise and effective post-processing method is proposed to refine the coarse segmentation result of ERV-Net. The experimental results on the dataset of multimodal brain tumor segmentation challenge 2018 (BRATS 2018) demonstrate that ERV-Net achieves the best performance with Dice of 81.8%, 91.21% and 86.62% and Hausdorff distance of 2.70 mm, 3.88 mm and 6.79 mm for enhancing tumor, whole tumor and tumor core, respectively. Besides, ERV-Net also achieves high efficiency compared to the state-of-the-art methods.

关键词： Brain tumor segmentation 3D convolutional neural network encoder-decoder Efficiency Lightweight Residual block

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Road extraction from remote sensing images based on a multi-scale asymmetric dual attention mechanism

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REMOTE SENSING LETTERS 2024年第8期15卷 751-761页

作者： Qu, Shenming Liu, Suchen Han, Fengyu Xie, Yuan Henan Univ Software Coll Henan Int Joint Lab Theories & Key Technol Intelli Kaifeng 475004 Henan Peoples R China

Aiming at the problems of road fracture and detail loss caused by not considering the geometric features in the road extraction method. We proposed an encoder-decoder architecture based on a multi-scale asymmetric dual attention mechanism. Firstly, A multi-scale convolution block in the shape of 'Union Jack' is designed. It includes symmetric convolution and asymmetric convolution along horizontal, vertical, left diagonal, and right diagonal spatial directions, and a multi-scale dilated convolution for extracting features of different scales. Remote dependence relationships are highly converged by using it, and road fracture problems caused by occlusion can be solved effectively. Secondly, a directional dual attention mechanism is proposed, which consists of directional channel attention using strip pooling and a directional spatial attention mechanism using asymmetric convolution along left diagonal and right diagonal spatial directions. It can use the directivity of asymmetric convolution to allocate attention mechanism adaptively in attention mechanism, and effectively avoid the road detail loss problem. Finally, we conducted corresponding experiments on the DeepGlobe and Ottawa road datasets, and the experimental results are superior to the current state-of-the-art methods.

关键词： Road extraction multi-scale asymmetric convolution asymmetric dual attention mechanism dilated convolution strip pooling encoder-decoder

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Multi-scale dual-channel feature embedding decoder for biomedical image segmentation

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 257卷 108464-108464页

作者： Agarwal, Rohit Ghosal, Palash Sadhu, Anup K. Murmu, Narayan India, Debashis Nandi Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 West Bengal India Sikkim Manipal Univ Sikkim Manipal Inst Technol Dept Informat Technol Sikkim India EKO Diagnost Ctr Med Coll Kolkata India

Background and Objective: Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical image segmentation. Several transformer-based models have been proposed to handle this issue in biomedical image segmentation. Despite this, segmentation accuracy remains an ongoing challenge, as these models often fall short of the target range due to their limited capacity to capture critical local and global contexts. However, the quadratic computational complexity is the main limitation of these models. Moreover, a large dataset is required to train those models. Methods: In this paper, we propose a novel multi-scale dual-channel decoder to mitigate this issue. The complete segmentation model uses two parallel encoders and a dual-channel decoder. The encoders are based on convolutional networks, which capture the features of the input images at multiple levels and scales. The decoder comprises a hierarchy of Attention-gated Swin Transformers with a fine-tuning strategy. The hierarchical Attention-gated Swin Transformers implements a multi-scale, multi-level feature embedding strategy that captures short and long-range dependencies and leverages the necessary features without increasing computational load. At the final stage of the decoder, a fine-tuning strategy is implemented that refines the features to keep the rich features and reduce the possibility of over-segmentation. Results: The proposed model is evaluated on publicly available LiTS, 3DIRCADb, and spleen datasets obtained from Medical Segmentation Decathlon. The model is also evaluated on a private dataset from Medical College Kolkata, India. We observe that the proposed model outperforms the state-of-the-art models in liver tumor and spleen segmentation in terms of evaluation metrics at a comparative computational cost. Conclusion: The novel dual-channel deco

关键词： Attention gate Biomedical image segmentation encoder-decoder Self-attention Vision transformer

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AdamR-GRUs: Adaptive momentum-based Regularized GRU for HMER problems

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APPLIED SOFT COMPUTING 2023年 143卷

作者： Pal, Aniket Singh, Krishna Pratap Indian Inst Informat Technol Dept Informat Technol Machine Learning & Optimizat Lab Allahabad India

Handwritten Mathematical Expression Recognition (HMER) is essential to online education and scientific research. However, discerning the length and characters of handwritten mathematical expressions remains a formidable challenge. In this study, we propose an encoder-decoder architecture incorporating a novel Adaptive momentum-based Regularized Gated Recurrent Unit (AdamR-GRU) to solve this problem. It incorporates a novel gate within the GRU framework to maintain input histories, enabling extended memory retention and faster convergence. To assess the performance of AdamR-GRU, we conducted a series of comprehensive experiments on the CROHME 2014, 2016, and 2019 datasets. The results indicate that the AdamR-GRU model exhibits competitive performance with existing state-of-the-art methods regarding Expression Recognition Rate (exprate). Moreover, our model achieves state-of-the-art accuracy concerning 1, 2, and 3 symbol-level errors (top 1, 2, 3 error accuracy) on the CROHME 2014 and 2016 datasets. For the CROHME 2019 dataset, the model attains state-of-the-art performance in top 2 and 3 error accuracy. AdamR-GRU demonstrates reduced computational complexity and memory usage compared to alternative contemporary models.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Handwritten Mathematical Expression Recognition encoder-decoder Gated recurrent unit Dropout Regularization Adaptive momentum

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BGNet: Boundary-guided network for polyp segmentation

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第1期34卷 e22959-e22959页

作者： Gao, Shanglei Zhan, Yinwei Chen, Zijun Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

Colonoscopy is one of the most direct and effective methods for detecting colon polyps;they are crucial for early screening and prevention of colorectal cancer (CRC). Accurate segmentation of polyp images is significant for the clinical management and treatment of CRC. However, polyp image segmentation is a challenging task because polyps vary in size, shape, and color, and have low contrast with the surrounding tissue and mucosa. To address these challenges, we propose a novel network called BGNet for polyp segmentation. BGNet consists of three modules: a boundary feature extraction module (BFEM), a mutual optimization module (MOM), and a convolutional attention module (CBAM). The BFEM extracts boundary features and predicts boundary maps, which complement and guide region features to accurately predict polyp regions and help BGNet generate precise prediction masks. The MOM optimizes the extracted features to enhance fine-grained feature representation. The CBAM performs attention operations on the feature map to focus on informative regions and suppress irrelevant information. We conduct quantitative and qualitative evaluations on five benchmark datasets, and the results show that BGNet outperforms other methods and has strong learning ability and generalization ability.

关键词： boundary-guided colorectal cancer (CRC) encoder-decoder polyp image segmentation

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Thermal wave image deblurring based on depth residual network

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INFRARED PHYSICS & TECHNOLOGY 2021年 117卷 103847-103847页

作者： Jiang, Haijun Chen, Fei Liu, Xining Chen, Jesse Zhang, Kai Li Chen Univ Elect Sci & Technol 2006 Xiyuan Blvd Chengdu 611731 Peoples R China Nanjing Univ Sci & Technol 200 Xiaolingwei St Nanjing 210094 Peoples R China Novelteq Ltd 6 Xingzhi Rd Nanjing 210046 Peoples R China

Thermal wave imaging is a nondestructive testing (NDT) technology widely used to detect defects for various materials. It is important for quality control purposes to be able to clearly define the sizes of the defective areas. Due to the diffusive nature of thermal waves the acquired images contain varying degrees of blur depending on the depth of the defects, which severely affects the ability to define the defects. Conventional edge enhancement algorithms are hardly to achieve desirable results. Using deep convolutional neural network, we designed a deep residual network based on an encoder-decoder structure. Through the depth residual and skip-connection structures, we can effectively solve the vanishing gradient problem and improve the ability of feature extraction. The experimental results demonstrate that the proposed method shows superior performance over conventional image enhancement algorithms by providing richer information with higher contrast and more details.

关键词： Infrared thermography Thermal wave image Image deblurring Depth residual network encoder-decoder

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CrackF-Net: a pixel-level segmentation network for pavement crack detection

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JOURNAL OF ELECTRONIC IMAGING 2023年第6期32卷 063002-063002页

作者： Luan, Shen Gao, Xingen Wang, Chen Zhang, Hongyi Chao, Fei Lin, Juqiang Huang, Junqi Jiang, Huali Lin, Feng Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Xiamen Peoples R China

Detecting pavement cracks from images is a complex computer vision task due to their varying shapes, backgrounds, and sizes. We propose CrackF-Net, an end-to-end convolutional neural network for automatic crack detection in road images. We construct the CrackF-Net network using an encoder-decoder architecture to extract image features in convolutional blocks with residuals and fuse the multiscale convolutional features produced by the decoder. Convolutional blocks with residuals are used to capture the strong semantic features of cracks, and an adaptive filter fusion module is proposed to assist the network make a selection of filter fusion features on the channels. CrackF-Net fuses the multiscale features in decoder to improve crack detection performance. The proposed CrackF-Net is compared to other advanced crack detection methods using three public datasets. The experimental results show that CrackF-Net achieves state-of-the-art performance, which obtains F-measures of 0.866, 0.737, and 0.852 on the three datasets. (c) 2023 SPIE and IS&T

关键词： crack detection encoder-decoder noise processing attention mechanism semantic segmentation

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An attention based dual learning approach for video captioning

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APPLIED SOFT COMPUTING 2022年第0期117卷 108332-108332页

作者： Ji, Wanting Wang, Ruili Tian, Yan Wang, Xun Liaoning Univ Sch Informat Shenyang Peoples R China Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn Hangzhou Peoples R China

Video captioning aims to generate sentences/captions to describe video contents. It is one of the key tasks in the field of multimedia processing. However, most of the current video captioning approaches utilize only the visual information of a video to generate captions. Recently, a new encoder-decoderreconstructor architecture was developed for video captioning, which can capture the information in both raw videos and the generated captions through dual learning. Based on this architecture, this paper proposes a novel attention based dual learning approach (ADL) for video captioning. Specifically, ADL is composed of a caption generation module and a video reconstruction module. The caption generation module builds a translatable mapping between raw video frames and the generated video captions, i.e., using the visual features extracted from videos by an Inception-V4 network to produce video captions. Then the video reconstruction module reproduces raw video frames using the generated video captions, i.e., using the hidden states of the decoder in the caption generation module to reproduce/synthesize raw visual features. A multi-head attention mechanism is adopted to help the two modules focus on the most effective information in videos and captions, and a dual learning mechanism is adopted to fine-tune the performance of the two modules to generate final video captions. Therefore, ADL can minimize the semantic gap between raw videos and the generated captions by minimizing the differences between the reproduced and the raw videos, thereby improving the quality of the generated video captions. Experimental results demonstrate that ADL is superior to the state-of-the-art video captioning approaches on benchmark datasets. (C) 2021 Published by Elsevier B.V.

关键词： Attention mechanism Deep neural network Dual learning encoder-decoder Video captioning

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A novel image-dehazing network with a parallel attention block

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PATTERN RECOGNITION 2020年第0期102卷 107255-000页

作者： Yin, Shibai Wang, Yibin Yang, Yee-Hong Southwestern Univ Finance & Econ Dept Econ Informat Engn Chengdu 611130 Sichuan Peoples R China Sichuan Normal Univ Dept Engn Chengdu 610066 Sichuan Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

Image dehazing is a very important pre-processing step to many computer vision tasks such as object recognition and tracking. However, it is a challenging problem because the physical parameters of imaging, e.g. the depth information of scene pixels and the attenuation model, are usually unknown. Based on a physical model, different methods have been proposed to recover these parameters. Existing convolutional neural networks (CNNs) based methods try to solve the image dehazing problem using an end-to-end network to learn a direct mapping between a hazy image and its corresponding clear image. But the representational ability, spatial variant ability and dehazing capability of these network models are hindered by treating all the spatial and channel-wise features indiscriminately. Hence, we propose an end-to-end dehazing network with a parallel spatial/channel-wise attention block for capturing more informative spatial and channel-wise features respectively. Specifically, based on the encoder-decoder framework with a pyramid pooling operation, a novel parallel spatial/channel-wise attention block is proposed and applied to the end of the encoder for guiding the decoder to reconstruct better clear images. In the spatial/channel-wise attention block, the spatial attention module and the channel-wise attention module are connected in parallel, where the spatial attention module highlights important spatial positions of features. Meanwhile, the channel-wise module exploits inter-dependencies among the channel-wise features. Extensive experiments demonstrate that our network with a parallel spatial/channel-wise attention block can achieve better accuracy and visual results over state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Image dehazing Spatial attention mechanism Channel-wise attention mechanism encoder-decoder CNN

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