FDAM: full-dimension attention module for deep convolutional neural networks

Cai, Silin; Wang, Changping; Ding, Jiajun; Yu, Jun; Fan, Jianping

doi:10.1007/s13735-022-00248-3

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Also, semantic segmentation can predict the category, position and shape of each element [4]. In particular, it is widely used in applications that require estimating the precise boundaries of an object [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

Doğan,

Ergen

2024

Int J Multimed Info Retr

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Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.

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Section: Introductionmentioning

confidence: 99%

A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

Doğan,

Ergen

2024

Int J Multimed Info Retr

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Insulator defect detection based on BaS-YOLOv5

Zhang,

Dou,

Yang

et al. 2024

Multimedia Systems

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Two-Phase Flow Pattern Identification by Embedding Double Attention Mechanisms into a Convolutional Neural Network

Qiao

Guo

Huang

et al. 2023

JMSE

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There are inevitable multiphase flow problems in the process of subsea oil-gas acquisition and transportation, of which the two-phase flow involving gas and liquid is given much attention. The performance of pipelines and equipment in subsea systems is greatly affected by various flow patterns. As a result, correctly and efficiently identifying the flow pattern in a pipeline is critical for the oil and gas industry. In this study, two attention modules, the convolutional block attention module (CBAM) and efficient channel attention (ECA), are introduced into a convolutional neural network (ResNet50) to develop a gas–liquid two-phase flow pattern identification model, which is named CBAM-ECA-ResNet50. To verify the accuracy and efficiency of the proposed model, a collection of gas–liquid two-phase flow pattern images in a vertical pipeline is selected as the dataset, and data augmentation is employed on the training set data to enhance the generalization capability and comprehensive performance of the model. Then, comparison models similar to the proposed model are obtained by adjusting the order and number of the two attention modules in the two positions and by inserting other different attention modules. Afterward, ResNet50 and all proposed models are applied to classify and identify gas–liquid two-phase flow pattern images. As a result, the identification accuracy of the proposed CBAM-ECA-ResNet50 is observed to be the highest (99.62%). In addition, the robustness and complexity of the proposed CBAM-ECA-ResNet50 are satisfactory.

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FDAM: full-dimension attention module for deep convolutional neural networks

Cited by 3 publications

References 32 publications

A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

Insulator defect detection based on BaS-YOLOv5

Two-Phase Flow Pattern Identification by Embedding Double Attention Mechanisms into a Convolutional Neural Network

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