Fusion of Swin Transformer's Unet in crop weed identification
With the development of agricultural mechanization, image analysis technology is more and more widely used in the agricultural field, and the automatic identification and management of weeds in crops is a very critical link. Traditional manual weeding is inefficient and cannot meet the needs of large-scale mechanized operations.
The intelligent weeding system based on image processing and computer vision provides the possibility for accurate identification and accurate weeding of weeds in the crop field. In the production process of cash crops such as corn, different kinds of weeds will mix with crops, seriously affecting the growth of crops. Timely identification and removal of these weeds is critical to increasing crop yields.
For the task of weed identification in corn fields, researchers at home and abroad proposed various image analysis methods. In the early days, it was generally based on manual feature engineering, combined with classifiers to identify weeds. However, such methods are less effective in distinguishing between different types of weeds. With the development of deep learning, weed recognition technology based on convolutional neural network has gradually become mainstream.
This type of method can learn feature expressions end-to-end, and great progress has been made in the fine differentiation of weeds. Semantic segmentation technology is one of the important means of pixel-level recognition and understanding in images. In the weed identification task, the semantic segmentation model can output the precise boundary of the weed to support subsequent weeding operations.
As a typical encoding-decoding structure of fully convolutional network, Unet is widely used in semantic segmentation tasks in the fields of medical images and remote sensing images. However, Unet has the problem of weak context modeling ability and insufficient learning of local details. Research in recent years has begun to attempt to integrate transformer modules in Unet models to enhance its ability to model global contexts.
Experimental Methods:
A dataset containing RGB images of maize fields and corresponding weed segmentation labeling images was collected. The image resolution is 256x256 pixels. The dataset contains 3 common maize field weeds: Matan, Paulus chinensis, and Rib Fruit.
The images collected combine different lighting conditions, occlusion conditions, and weed species distribution. This dataset is representative of the training and testing of the model. The collection of data lays the foundation for the construction of efficient weed identification models in maize fields.
Compared with ordinary Unet, the Swin-Unet model proposed in this paper enhances the modeling ability of global context information by introducing the Swin Transformer module.
UNET adopts the encoding-decoding network structure, and the information loss in the encoding process is serious, resulting in insufficient learning of the global context relationship. To solve this problem, Swin-Unet introduced the Swin transformer module in the coding phase.
Each Swin transformer module integrates both local information and a range of global dependencies by calculating the self-attention weights within the local window.
In addition, Swin Transformer is organized in a hierarchical way, which can learn and fuse feature representations at different scales. This multi-scale modeling method strengthens the model's perception of the global scene.
During the decoding process, Swin-Unet preserves the upsampling and hopping connection structure of Unet. Stepwise upsampling can restore the spatial resolution, and the semantic information of the top layer is also transmitted to the bottom layer, which helps to refine segmentation.
Jumping connections directly transmit coding features, further strengthening the use of local information. In this way, Swin-Unet has both global and local refinement modeling perspectives, and the semantic understanding of images is more comprehensive and detailed.
Compared with a single fully convolutional network, the semantic segmentation performance of Swin-Unet is significantly improved. Therefore, this network structure design provides strong support for efficient and accurate agricultural image analysis and intelligent weeding system.
Swin Transformer considers both local information and global context by calculating self-attention within the local window. In addition, the Swin module organizes features in a hierarchical manner to form a multi-scale feature representation.
This further improves the model's perception of the scene. In the decoding stage, Swin-Unet retains the upsampling and hopping operations of Unet, taking into account both global and local information expression. Finally, the DropBlock regularization technique is used to enhance the generalization ability of the model and effectively suppress the overfitting problem.
In experiments, compared with the original Unet, this improved Swin-Unet structure has achieved significant improvement in weed identification and semantic segmentation tasks.
The verification results show that the model can accurately identify different weed locations in the image, which provides a key visual analysis module for the intelligent weeding system
Conclusion:
In this paper, an automatic weed identification and segmentation method in maize field based on deep learning is studied. Aiming at the development status of image semantic segmentation, this paper proposes an improved Swin-Unet network structure. The model introduces the Swin transformer block in the encoder part, which enhances the ability to model the global context.
At the same time, the decoder part retains the multi-scale feature fusion structure of Unet, which improves the learning of local details. Compared with the original Unet, the improved Swin-Unet extracts richer feature representations and distinguishes different species of weeds better. In order to further improve the robustness of the model, the article adopts the DropBlock regularization technique.
DropBlock performs dropout by framing a certain shape of feature area, which strengthens the model's learning of local features and improves the generalization of the model. In addition, an image dataset containing common weeds in corn fields is constructed, which provides support for model training and evaluation.
By training the improved Swin-Unet model on this dataset and comparing it with other network structures, the effectiveness of the proposed method is verified. The optimized Swin-Unet model achieves a higher mean cross-union ratio, demonstrating a strong ability to identify weeds in maize fields. This technology provides a powerful technical means for the vision system of agricultural equipment such as intelligent weeding robots.
