Abstract
Semantic segmentation in high-resolution agricultural imagery demands models that strike a careful balance between accuracy and computational efficiency to enable deployment in practical systems. In this work, we propose DAS-SK, a novel lightweight architecture that retrofits selective kernel convolution (SK-Conv) into the dual atrous separable convolution (DAS-Conv) module to strengthen multi-scale feature learning. The model further enhances the atrous spatial pyramid pooling (ASPP) module, enabling the capture of fine-grained local structures alongside global contextual information. Built upon a modified DeepLabV3 framework with two complementary backbones - MobileNetV3-Large and EfficientNet-B3, the DAS-SK model mitigates limitations associated with large dataset requirements, limited spectral generalization, and the high computational cost that typically restricts deployment on UAVs and other edge devices. Comprehensive experiments across three benchmarks: this http URL, VDD, and PhenoBench, demonstrate that DAS-SK consistently achieves state-of-the-art performance, while being more efficient than CNN-, transformer-, and hybrid-based competitors. Notably, DAS-SK requires up to 21x fewer parameters and 19x fewer GFLOPs than top-performing transformer models. These findings establish DAS-SK as a robust, efficient, and scalable solution for real-time agricultural robotics and high-resolution remote sensing, with strong potential for broader deployment in other vision domains.
Abstract (translated)
在高分辨率农业图像的语义分割任务中，需要一种模型能够巧妙地平衡精度和计算效率，以便能够在实际系统中部署。在这项工作中，我们提出了一种新颖且轻量级的架构DAS-SK（Dual Atrous Separable Convolution with Selective Kernel）。该架构将选择性核卷积（SK-Conv）融入到双空洞可分离卷积模块（DAS-Conv）中，以增强多尺度特征学习能力。此外，模型还改进了空洞空间金字塔池化（ASPP）模块，使其能够捕获精细的局部结构和全局上下文信息。 该模型基于修改后的DeepLabV3框架，并采用了两个互补的骨干网络：MobileNetV3-Large和EfficientNet-B3。通过这种方式，DAS-SK模型克服了大数据集需求、有限光谱泛化能力和高计算成本这些限制因素，后者通常会阻碍在无人机（UAV）和其他边缘设备上的部署。 在三个基准测试集（这个链接已给出但未显示具体信息的URL、VDD和PhenoBench）上进行的全面实验表明，DAS-SK始终能够达到最先进的性能，并且比基于CNN、Transformer及混合架构的竞争模型更高效。尤其值得注意的是，与顶级表现的Transformer模型相比，DAS-SK所需参数少至21倍，计算量（GFLOPs）少至19倍。 这些研究结果表明，DAS-SK作为实时农业机器人和高分辨率遥感应用的一种强大、高效且可扩展的解决方案具有巨大潜力，并且在其他视觉领域中也具备广泛部署的可能性。
URL
https://arxiv.org/abs/2602.08168
