标题： 双空洞可分离卷积用于提高农业语义分割 显示英文标题

标题： Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation

摘要： 农业图像语义分割是现代农业的一个关键组成部分，有助于准确的视觉数据分析，以改善作物管理、优化资源利用并提高整体生产力。 本研究提出了一种用于精准农业的高效图像分割方法，重点是准确划分农田异常，以支持决策和主动干预。 在基于DeepLabV3的分割框架中集成了一个新颖的双空洞可分离卷积（DAS Conv）模块。 DAS Conv模块经过精心设计，以在膨胀率和填充大小之间达到最佳平衡，从而在不牺牲效率的情况下提高模型性能。 该研究还结合了从编码器中最佳阶段到解码器的战略跳跃连接，以增强模型捕捉细粒度空间特征的能力。 尽管计算复杂度较低，所提出的模型优于基线模型，并在Agriculture Vision基准数据集上实现了与高度复杂的基于变压器的最先进（SOTA）模型相当的性能。 在考虑模型复杂性和性能之间的权衡时，与SOTA模型相比，该模型在效率方面提高了超过66%。 本研究突出了一个高效且有效的解决方案，用于改进遥感应用中的语义分割，提供了一个计算轻量级的模型，能够在农业图像中实现高质量的性能。 显示英文摘要

摘要： Agricultural image semantic segmentation is a pivotal component of modern agriculture, facilitating accurate visual data analysis to improve crop management, optimize resource utilization, and boost overall productivity. This study proposes an efficient image segmentation method for precision agriculture, focusing on accurately delineating farmland anomalies to support informed decision-making and proactive interventions. A novel Dual Atrous Separable Convolution (DAS Conv) module is integrated within the DeepLabV3-based segmentation framework. The DAS Conv module is meticulously designed to achieve an optimal balance between dilation rates and padding size, thereby enhancing model performance without compromising efficiency. The study also incorporates a strategic skip connection from an optimal stage in the encoder to the decoder to bolster the model's capacity to capture fine-grained spatial features. Despite its lower computational complexity, the proposed model outperforms its baseline and achieves performance comparable to highly complex transformer-based state-of-the-art (SOTA) models on the Agriculture Vision benchmark dataset. It achieves more than 66% improvement in efficiency when considering the trade-off between model complexity and performance, compared to the SOTA model. This study highlights an efficient and effective solution for improving semantic segmentation in remote sensing applications, offering a computationally lightweight model capable of high-quality performance in agricultural imagery.