根据图像质量动态选择模型，提升农田杂草分割精度与效率
Modular Transformer Architecture for Precision Agriculture Imaging
- 按模糊、噪声程度自动路由图像至对应优化的Transformer模型
- 在多个农业数据集上达到更高分割准确率，且推理速度更快
- 适合需要实时处理无人机影像的智慧农业系统使用
本文针对精准农业中无人机视频的杂草分割需求，提出一种质量感知的模块化深度学习框架。该框架通过均值绝对偏差和拉普拉斯算子分析图像的模糊与噪声水平，动态将输入路由至三个专用视觉Transformer：用于清晰图像的基线模型，采用费舍尔向量编码以降噪的改进模型，以及嵌入展开式Lucy-Richardson解码器以校正模糊的模型。实验表明，该方法在多个农业数据集上优于现有基于CNN的方法，在分割质量与计算效率方面均有显著提升，推动了深度学习在农业场景中的应用进展。
原文摘要 · Abstract (English)
This paper addresses the critical need for efficient and accurate weed segmentation from drone video in precision agriculture. A quality-aware modular deep-learning framework is proposed that addresses common image degradation by analyzing quality conditions-such as blur and noise-and routing inputs through specialized pre-processing and transformer models optimized for each degradation type. The system first analyzes drone images for noise and blur using Mean Absolute Deviation and the Laplacian. Data is then dynamically routed to one of three vision transformer models: a baseline for clean images, a modified transformer with Fisher Vector encoding for noise reduction, or another with an unrolled Lucy-Richardson decoder to correct blur. This novel routing strategy allows the system to outperform existing CNN-based methods in both segmentation quality and computational efficiency, demonstrating a significant advancement in deep-learning applications for agriculture.
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