用AI生成假图像提升农田杂草识别准确率
Enhancing weed detection performance by means of GenAI-based image augmentation
- 用Stable Diffusion生成多样合成图像扩充数据集
- YOLO纳米模型在增强数据上mAP50提升显著
- 适合边缘设备部署的轻量级检测系统
精准杂草管理对维持作物产量和生态平衡至关重要。传统除草剂使用面临经济与环境挑战，亟需基于深度学习的智能控制方案。此类系统依赖大量高质量训练数据，但真实标注数据稀缺，常通过数据增强补充。然而，常规增强方法（如随机翻转、调色、模糊）缺乏真实感与多样性。本文提出利用Stable Diffusion模型生成高保真、多样化的合成图像，用于扩充杂草检测数据集。实验评估了这些合成数据对实时检测系统的影响，聚焦于适用于边缘设备的轻量级卷积神经网络（如YOLO nano）。结果表明，采用生成式AI增强数据训练的YOLO模型在mAP50与mAP50-95指标上均有显著提升，证明合成数据能有效增强模型鲁棒性与精度。
原文摘要 · Abstract (English)
Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered by deep learning. These systems require vast amounts of high-quality training data. The reality of scarcity of well-annotated training data, however, is often addressed through generating more data using data augmentation. Nevertheless, conventional augmentation techniques such as random flipping, color changes, and blurring lack sufficient fidelity and diversity. This paper investigates a generative AI-based augmentation technique that uses the Stable Diffusion model to produce diverse synthetic images that improve the quantity and quality of training datasets for weed detection models. Moreover, this paper explores the impact of these synthetic images on the performance of real-time detection systems, thus focusing on compact CNN-based models such as YOLO nano for edge devices. The experimental results show substantial improvements in mean Average Precision (mAP50 and mAP50-95) scores for YOLO models trained with generative AI-augmented datasets, demonstrating the promising potential of synthetic data to enhance model robustness and accuracy.
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