DedustNet：一种用于农业除尘的频率主导的基于Swin Transformer的小波网络
DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network for Agricultural Dust Removal
AI总结：
针对农业机械在多尘环境中的感知问题，提出端到端网络DedustNet，首次将Swin Transformer单元与小波变换结合，并引入跨层级融合与空洞卷积模块，有效去除图像灰尘并保留结构特征，性能优于现有方法。
摘要：
尽管灰尘严重影响了自动化农业机械的环境感知能力，但现有的基于深度学习的除尘方法在该领域仍需进一步研究与改进，以提升自动化农业机械在农业中的性能和可靠性。我们提出了一种端到端可训练的学习网络（DedustNet）来解决真实的农业除尘任务。据我们所知，这是首次将基于Swin Transformer的单元应用于小波网络中进行农业图像除尘。具体而言，我们通过在Swin Transformer中添加空间特征聚合方案（SFAS）并将其与小波变换相结合，提出了频率主导模块（DWTFormer模块和IDWTFormer模块），从而缓解了Swin Transformer在处理复杂灰尘背景时全局感受野的限制。此外，我们提出了一个跨层级信息融合模块，用于融合不同层级的特征，并有效捕获全局和长距离特征关系。另外，我们提出了一个空洞卷积模块，用于在多尺度下捕获由小波变换引导的上下文信息，该模块结合了小波变换和空洞卷积的优势。我们的算法利用深度学习技术有效地去除了图像中的灰尘，同时保留了原始的结构和纹理特征。与现有的最先进方法相比，DedustNet在农业图像除尘中取得了优越的性能和更可靠的结果，为农业机械在多尘环境中的应用提供了强有力的支持。此外，在真实世界的雾化数据集和应用测试上的优异表现，突显了DedustNet卓越的泛化能力和计算机视觉相关应用的性能。
英文摘要：
While dust significantly affects the environmental perception of automated agricultural machines, the existing deep learning-based methods for dust removal require further research and improvement in this area to improve the performance and reliability of automated agricultural machines in agriculture. We propose an end-to-end trainable learning network (DedustNet) to solve the real-world agricultural dust removal task. To our knowledge, DedustNet is the first time Swin Transformer-based units have been used in wavelet networks for agricultural image dusting. Specifically, we present the frequency-dominated block (DWTFormer block and IDWTFormer block) by adding a spatial features aggregation scheme (SFAS) to the Swin Transformer and combining it with the wavelet transform, the DWTFormer block and IDWTFormer block, alleviating the limitation of the global receptive field of Swin Transformer when dealing with complex dusty backgrounds. Furthermore, We propose a cross-level information fusion module to fuse different levels of features and effectively capture global and long-range feature relationships. In addition, we present a dilated convolution module to capture contextual information guided by wavelet transform at multiple scales, which combines the advantages of wavelet transform and dilated convolution. Our algorithm leverages deep learning techniques to effectively remove dust from images while preserving the original structural and textural features. Compared to existing state-of-the-art methods, DedustNet achieves superior performance and more reliable results in agricultural image dedusting, providing strong support for the application of agricultural machinery in dusty environments. Additionally, the impressive performance on real-world hazy datasets and application tests highlights DedustNet superior generalization ability and computer vision-related application performance.
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