论文标题
基于双重注意的轻巧网络用于植物害虫识别
Double Attention-based Lightweight Network for Plant Pest Recognition
论文作者
论文摘要
及时对现场图像的植物害虫的认识非常重要，以避免作物产量的潜在损失。传统的基于卷积神经网络的深度学习模型需要高计算能力，并需要每种害虫类型的大量标记样品进行训练。另一方面，由于多个植物害虫之间的共同特征和高度相似性，现有的基于轻质网络的方法在正确地对害虫进行了正确分类。在这项工作中，提出了一种新型的基于双重注意的轻型深度学习体系结构，以自动识别不同的植物害虫。轻量级网络通过关注最相关的信息来提高更快和小的数据培训，而双重注意模块可以提高性能。提出的方法分别在两个公开可用数据集的三种变体中分别获得了96.61％，99.08％和91.60％，分别为5869、545和500个样本。此外，比较结果表明，所提出的方法始终超过小型数据集上的现有方法。
Timely recognition of plant pests from field images is significant to avoid potential losses of crop yields. Traditional convolutional neural network-based deep learning models demand high computational capability and require large labelled samples for each pest type for training. On the other hand, the existing lightweight network-based approaches suffer in correctly classifying the pests because of common characteristics and high similarity between multiple plant pests. In this work, a novel double attention-based lightweight deep learning architecture is proposed to automatically recognize different plant pests. The lightweight network facilitates faster and small data training while the double attention module increases performance by focusing on the most pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60% on three variants of two publicly available datasets with 5869, 545 and 500 samples, respectively. Moreover, the comparison results reveal that the proposed approach outperforms existing approaches on both small and large datasets consistently.
