scholarly journals Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

2022 ◽  
Vol 9 ◽  
Author(s):  
Zhiqiang Hao ◽  
Zhigang Wang ◽  
Dongxu Bai ◽  
Bo Tao ◽  
Xiliang Tong ◽  
...  
Keyword(s):  
Network Model ◽  
Production Efficiency ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Smart Manufacturing ◽  
Attention Network ◽  
Multi Scale ◽  
Intelligent Detection ◽  
Split Attention ◽  
Visual Attention Mechanism

The intelligent monitoring and diagnosis of steel defects plays an important role in improving steel quality, production efficiency, and associated smart manufacturing. The application of the bio-inspired algorithms to mechanical engineering problems is of great significance. The split attention network is an improvement of the residual network, and it is an improvement of the visual attention mechanism in the bionic algorithm. In this paper, based on the feature pyramid network and split attention network, the network is improved and optimised in terms of data enhancement, multi-scale feature fusion and network structure optimisation. The DF-ResNeSt50 network model is proposed, which introduces a simple modularized split attention block, which can improve the attention mechanism of cross-feature graph groups. Finally, experimental validation proves that the proposed network model has good performance and application prospects in the intelligent detection of steel defects.

scholarly journals A new end-to-end image dehazing algorithm based on residual attention mechanism

2021 ◽  
Vol 39 (4) ◽  
pp. 901-908
Author(s):  
Zhenjian Yang ◽  
Jiamei Shang ◽  
Zhongwei Zhang ◽  
Yan Zhang ◽  
Shudong Liu
Keyword(s):  
Feature Extraction ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Scale Feature ◽  
Multi Scale ◽  
Color Distortion ◽  
End To End ◽  
Density Image

Traditional image dehazing algorithms based on prior knowledge and deep learning rely on the atmospheric scattering model and are easy to cause color distortion and incomplete dehazing. To solve these problems, an end-to-end image dehazing algorithm based on residual attention mechanism is proposed in this paper. The network includes four modules: encoder, multi-scale feature extraction, feature fusion and decoder. The encoder module encodes the input haze image into feature map, which is convenient for subsequent feature extraction and reduces memory consumption; the multi-scale feature extraction module includes residual smoothed dilated convolution module, residual block and efficient channel attention, which can expand the receptive field and extract different scale features by filtering and weighting; the feature fusion module with efficient channel attention adjusts the channel weight dynamically, acquires rich context information and suppresses redundant information so as to enhance the ability to extract haze density image of the network; finally, the encoder module maps the fused feature nonlinearly to obtain the haze density image and then restores the haze free image. The qualitative and quantitative tests based on SOTS test set and natural haze images show good objective and subjective evaluation results. This algorithm improves the problems of color distortion and incomplete dehazing effectively.

Multi-Scale Attention Network Based on Multi-Feature Fusion for Person Re-Identification

2021 ◽  
Author(s):  
Minghao Li ◽  
Liming Yuan ◽  
Xianbin Wen ◽  
Jianchen Wang ◽  
Gengsheng Xie ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Attention Network ◽  
Multi Scale

Assessment of feature fusion strategies in visual attention mechanism for saliency detection

2019 ◽  
Vol 127 ◽  
pp. 37-47 ◽  
Author(s):  
Muwei Jian ◽  
Quan Zhou ◽  
Chaoran Cui ◽  
Xiushan Nie ◽  
Hanjiang Luo ◽  
...  
Keyword(s):  
Visual Attention ◽  
Feature Fusion ◽  
Saliency Detection ◽  
Attention Mechanism ◽  
Visual Attention Mechanism

scholarly journals Subtask Attention Based Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1925
Author(s):  
Shengzhou Xiong ◽  
Yihua Tan ◽  
Yansheng Li ◽  
Cai Wen ◽  
Pei Yan
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Task ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Attention Network ◽  
Multi Scale ◽  
Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

A Novel Detection Method of Paper Defects Based on Visual Attention Mechanism

2011 ◽  
Vol 3 (3) ◽  
pp. 24-31 ◽  
Author(s):  
Ping Jiang ◽  
Tao Gao
Keyword(s):  
Visual Attention ◽  
Detection Method ◽  
Saliency Map ◽  
Attention Mechanism ◽  
Feature Maps ◽  
Seed Point ◽  
Multi Scale ◽  
Comparative Maps ◽  
Salient Points ◽  
Visual Attention Mechanism

In this paper, an improved paper defects detection method based on visual attention mechanism computation model is presented. First, multi-scale feature maps are extracted by linear filtering. Second, the comparative maps are obtained by carrying out center-surround difference operator. Third, the saliency map is obtained by combining conspicuity maps, which is gained by combining the multi-scale comparative maps. Last, the seed point of watershed segmentation is determined by competition among salient points in the saliency map and the defect regions are segmented from the background. Experimental results show the efficiency of the approach for paper defects detection.

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