DenseNet weed recognition model combining local variance preprocessing and attention mechanism

Ye, Mu; Ni, Ruiwen; Fu, Lili; Tianye, Luo; Feng, Ruilong; Li, Ji; Pan, Haohong; Wang, Yingkai; Sun, Yu; Gong, He; Guo, Ying; Hu, Tianli; Yu, Bin; Li, Shijun

doi:10.3389/fpls.2022.1041510

Cited by 6 publications

(4 citation statements)

References 25 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Guo et al (2021) propose a lightweight universal attention module based on channel attention and spatial attention for image classification task. Mu et al (2023) address the problem of weed species recognition by introducing an efficient channel attention mechanism. In addition, in medical image analysis, the channel attention mechanism also shows great potential ability on improving model performance.…”

Section: Channel Attention Mechanismmentioning

confidence: 99%

“…Srivastava et al (2022) propose to employ channel attention module to address the multi-label cardiac anomaly classification problem. Mu et al (2023) propose to develop CRAL based on channel attention mechanism for chest x-ray classification problem. propose a triple-attention model to improve chest x-ray recognition performance.…”

Section: Channel Attention Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

Chest x-ray diagnosis via spatial-channel high-order attention representation learning

Gao,

Jiang,

Wang

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Objective. Chest x-ray image representation and learning is an important problem in computer-aided diagnostic area. Existing methods usually adopt CNN or Transformers for feature representation learning and focus on learning effective representations for chest x-ray images. Although good performance can be obtained, however, these works are still limited mainly due to the ignorance of mining the correlations of channels and pay little attention on the local context-aware feature representation of chest x-ray image. Approach. To address these problems, in this paper, we propose a novel spatial-channel high-order attention model (SCHA) for chest x-ray image representation and diagnosis. The proposed network architecture mainly contains three modules, i.e. CEBN, SHAM and CHAM. To be specific, firstly, we introduce a context-enhanced backbone network by employing multi-head self-attention to extract initial features for the input chest x-ray images. Then, we develop a novel SCHA which contains both spatial and channel high-order attention learning branches. For the spatial branch, we develop a novel local biased self-attention mechanism which can capture both local and long-range global dependences of positions to learn rich context-aware representation. For the channel branch, we employ Brownian Distance Covariance to encode the correlation information of channels and regard it as the image representation. Finally, the two learning branches are integrated together for the final multi-label diagnosis classification and prediction. Main results. Experiments on the commonly used datasets including ChestX-ray14 and CheXpert demonstrate that our proposed SCHA approach can obtain better performance when comparing many related approaches. Significance. This study obtains a more discriminative method for chest x-ray classification and provides a technique for computer-aided diagnosis.

show abstract

Section: Channel Attention Mechanismmentioning

confidence: 99%

Section: Channel Attention Mechanismmentioning

confidence: 99%

Chest x-ray diagnosis via spatial-channel high-order attention representation learning

Gao,

Jiang,

Wang

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…However, due to the use of SVM as a classifier, the model's runtime is relatively slow. Mu et al (2023) introduced an ECA attention mechanism into a DenseNet weed classification model. Through comparative experiments with DenseNet, on processed weed image datasets, the improved model achieved the best accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improving U-net network for semantic segmentation of corns and weeds during corn seedling stage in field

Cui,

Tan,

Bai

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionWeeds are one of the main factors affecting crop growth, making weed control a pressing global problem. In recent years, interest in intelligent mechanical weed-control equipment has been growing. MethodsWe propose a semantic segmentation network, RDS_Unet, based on corn seedling fields built upon an improved U-net network. This network accurately recognizes weeds even under complex environmental conditions, facilitating the use of mechanical weeding equipment for reducing weed density. Our research utilized field-grown maize seedlings and accompanying weeds in expansive fields. We integrated the U-net semantic segmentation network, employing ResNeXt-50 for feature extraction in the encoder stage. In the decoder phase, Layer 1 uses deformable convolution with adaptive offsets, replacing traditional convolution. Furthermore, concurrent spatial and channel squeeze and excitation is incorporated after ordinary convolutional layers in Layers 2, 3, and 4. ResultsCompared with existing classical semantic segmentation models such as U-net, Pspnet, and DeeplabV3, our model demonstrated superior performance on our specially constructed seedling grass semantic segmentation dataset, CGSSD, during the maize seedling stage. The Q6mean intersection over union (MIoU), precision, and recall of this network are 82.36%, 91.36%, and 89.45%, respectively. Compared to those of the original network, the proposed network achieves improvements of 5.91, 3.50, and 5.49 percentage points in the MIoU, precision, and recall, respectively. The detection speed is 12.6 frames per second. In addition, ablation experiments further confirmed the impactful contribution of each improvement component on the overall semantic segmentation performance. DiscussionThis study provides theoretical and technical support for the automated operation of intelligent mechanical weeding devices.

show abstract

“…Based on the optimization and improvement of DenseNet, Mu et al [22] introduced an ECA efficient channel attention module, which improved the recognition accuracy of crop and weed to 97.98%. Zhao et al [38] proposed an optimized YOLOv4 model integrating CBAM hybrid attention mechanism for detecting weeds in potato fields, with a detection mAP value of 98.52%, which was superior to other commonly used target detection models.…”

Section: Introductionmentioning

confidence: 99%

Channel Coordination Attention for Crop and Weed Segmentation Neural Networks

Bai,

Xue,

Dai

et al. 2023

Preprint

View full text Add to dashboard Cite

Convolutional neural network is widely used in the field of intelligent agricultural production, but in weed control, the complexity of the environment and the similarity between plants place higher demands on the task of crop and weed image recognition and segmentation. The attention mechanism, as a plug and play module, has been shown to significantly improve the network performance by dynamically weighting the input images to achieve the adjustment of the neural network attention. In this paper, a hybrid attention mechanism was proposed to establish global feature dependence by using channel autocorrelation matrix, named channel coordination attention. The attention mechanism established an autocorrelation matrix by obtaining the dependency relationships of feature channels, and used this matrix to aggregate spatial features in different directions to strengthen the location features. The resulting pair of spatial attention and previously obtained channel attention worked together on the input feature map to enhance feature representation capability. This study establishes an Unet network model to compare the performance of the proposed attention mechanism with other mainstream attention in crop and weed segmentation application scenarios. The experimental results showed that the proposed attention mechanism outperformed current mainstream attention mechanisms, such as Squeeze-and-Excitation attention (SE), Convolutional Block Attention Module (CBAM), Coordinated Attention (CA), etc., in terms of interaction ratio, accuracy, and average pixel accuracy. Finally, by comparing the performance in different recognition algorithms, it was proven that the proposed attention mechanism can be applied to different network models and significantly improve its segmentation performance.

show abstract

DenseNet weed recognition model combining local variance preprocessing and attention mechanism

Cited by 6 publications

References 25 publications

Chest x-ray diagnosis via spatial-channel high-order attention representation learning

Chest x-ray diagnosis via spatial-channel high-order attention representation learning

Improving U-net network for semantic segmentation of corns and weeds during corn seedling stage in field

Channel Coordination Attention for Crop and Weed Segmentation Neural Networks

Contact Info

Product

Resources

About