Real-time segmentation of weeds in cornfields based on depthwise separable convolution residual network

Guo, Hao; Wang, Shengsheng; Lu, Yinan

doi:10.1504/ijcse.2020.113185

Cited by 3 publications

(2 citation statements)

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“…Currently, there are many semantic segmentation methods for crop weed segmentation based on the UNet model with simple modifications. Guo et al [49] added a depth-wise separable convolution residual to a UNet, assigning different weights to each channel of the feature map obtained based on the convolutional operations, and using adaptive backpropagation to adjust the size of the one-dimensional convolutional kernel. This module slightly increases the number of parameters, but improves the network's feature extraction performance and enhances attention on the channels.…”

Section: Discussionmentioning

confidence: 99%

MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field

Yang

et al. 2023

Agriculture

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Weed control has always been one of the most important issues in agriculture. The research based on deep learning methods for weed identification and segmentation in the field provides necessary conditions for intelligent point-to-point spraying and intelligent weeding. However, due to limited and difficult-to-obtain agricultural weed datasets, complex changes in field lighting intensity, mutual occlusion between crops and weeds, and uneven size and quantity of crops and weeds, the existing weed segmentation methods are unable to perform effectively. In order to address these issues in weed segmentation, this study proposes a multi-scale convolutional attention network for crop and weed segmentation. In this work, we designed a multi-scale feature convolutional attention network for segmenting crops and weeds in the field called MSFCA-Net using various sizes of strip convolutions. A hybrid loss designed based on the Dice loss and focal loss is used to enhance the model’s sensitivity towards different classes and improve the model’s ability to learn from hard samples, thereby enhancing the segmentation performance of crops and weeds. The proposed method is trained and tested on soybean, sugar beet, carrot, and rice weed datasets. Comparisons with popular semantic segmentation methods show that the proposed MSFCA-Net has higher mean intersection over union (MIoU) on these datasets, with values of 92.64%, 89.58%, 79.34%, and 78.12%, respectively. The results show that under the same experimental conditions and parameter configurations, the proposed method outperforms other methods and has strong robustness and generalization ability.

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Section: Discussionmentioning

confidence: 99%

MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field

Yang

et al. 2023

Agriculture

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“…Genze et al [ 8 ] used a residual neural network as a feature extractor to detect and segment weeds in sorghum fields and further published a manually annotated and expert-curated UAV image dataset that was able to exhibit good weed detection performance under motion-blurred capture conditions. Guo et al [ 9 ] used a lightweight network based on an encoder–decoder architecture with randomized split separable residual blocks to compress the model while increasing the number of network layers to extract richer pixel category information, which was optimized by a weighted cross-entropy loss function to improve the segmentation accuracy and real-time segmentation speed of the crop weed dataset. Jiang et al [ 10 ] developed a deep learning-based semantic segmentation model using Visual Transformer to classify and localize weed regions in grassy areas, which is capable of detecting weeds in a recovered grass environment.…”

Section: Introductionmentioning

confidence: 99%

A Weakly Supervised Semantic Segmentation Model of Maize Seedlings and Weed Images Based on Scrawl Labels

Zhao,

Liu

et al. 2023

Sensors

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The task of semantic segmentation of maize and weed images using fully supervised deep learning models requires a large number of pixel-level mask labels, and the complex morphology of the maize and weeds themselves can further increase the cost of image annotation. To solve this problem, we proposed a Scrawl Label-based Weakly Supervised Semantic Segmentation Network (SL-Net). SL-Net consists of a pseudo label generation module, encoder, and decoder. The pseudo label generation module converts scrawl labels into pseudo labels that replace manual labels that are involved in network training, improving the backbone network for feature extraction based on the DeepLab-V3+ model and using a migration learning strategy to optimize the training process. The results show that the intersection over union of the pseudo labels that are generated by the pseudo label module with the ground truth is 83.32%, and the cosine similarity is 93.55%. In the semantic segmentation testing of SL-Net for image seedling of maize plants and weeds, the mean intersection over union and average precision reached 87.30% and 94.06%, which is higher than the semantic segmentation accuracy of DeepLab-V3+ and PSPNet under weakly and fully supervised learning conditions. We conduct experiments to demonstrate the effectiveness of the proposed method.

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An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge

Le,

Tsiknos,

Carlson

et al. 2022

2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Real-time segmentation of weeds in cornfields based on depthwise separable convolution residual network

Cited by 3 publications

References 0 publications

MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field

MSFCA-Net: A Multi-Scale Feature Convolutional Attention Network for Segmenting Crops and Weeds in the Field

A Weakly Supervised Semantic Segmentation Model of Maize Seedlings and Weed Images Based on Scrawl Labels

An energy-efficient AkidaNet for morphologically similar weeds and crops recognition at the Edge

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