用中点归一化+注意力机制，提升植物病害图像分类与分割精度
Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism
- 先用中点归一化预处理图像，再用注意力机制动态调整特征
- 分类准确率93%，目标类别F1值完美；分割Dice达72.44%，IoU为58.54%
- 模型轻量高效，适合实际场景部署
由于标注数据稀缺和复杂上下文因素，从叶面图像中精准识别植物病害仍具挑战。本文提出一种两阶段方法：首先采用中点归一化（MPN）进行智能图像预处理，再结合先进的注意力机制动态重校特征表示。分类部分融合MPN与Squeeze-and-Excitation（SE）模块，实现93%的准确率，并保持优异的类别平衡性，目标类别的F1分数达到完美。在分割任务中，将相同的注意力模块集成至U-Net架构，输入经MPN增强，取得72.44%的Dice分数和58.54%的IoU，显著优于基线模型。该方法不仅性能优越，且具备计算高效、轻量化特点，适用于真实世界的计算机视觉应用。
原文摘要 · Abstract (English)
Enhancing plant disease detection from leaf imagery remains a persistent challenge due to scarce labeled data and complex contextual factors. We introduce a transformative two-stage methodology, Mid Point Normalization (MPN) for intelligent image preprocessing, coupled with sophisticated attention mechanisms that dynamically recalibrate feature representations. Our classification pipeline, merging MPN with Squeeze-and-Excitation (SE) blocks, achieves remarkable 93% accuracy while maintaining exceptional class-wise balance. The perfect F1 score attained for our target class exemplifies attention's power in adaptive feature refinement. For segmentation tasks, we seamlessly integrate identical attention blocks within U-Net architecture using MPN-enhanced inputs, delivering compelling performance gains with 72.44% Dice score and 58.54% IoU, substantially outperforming baseline implementations. Beyond superior accuracy metrics, our approach yields computationally efficient, lightweight architectures perfectly suited for real-world computer vision applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。