DeWi通过交替训练提升昆虫分类的准确率与泛化能力。
Deep-Wide Learning Assistance for Insect Pest Classification
- 采用单阶段交替训练，同时优化特征区分性与泛化能力。
- 在IP102和D0数据集上分别达到76.44%和99.79%准确率。
- 适合农业害虫识别场景，尤其适用于小样本多类别任务。
准确的昆虫害虫识别在农业中至关重要，但因昆虫特征复杂而具有挑战性。本文提出DeWi，一种新型的昆虫害虫分类学习辅助方法。通过单阶段交替训练策略，DeWi从两个方面同步改进多个卷积神经网络：通过监督式三元组间隔损失优化区分性，通过数据增强提升泛化能力。由此，DeWi能学习到深入且具区分性的害虫特征（深），同时对大量昆虫类别保持良好泛化能力（广）。实验结果表明，DeWi在两个昆虫分类基准上均取得最高性能：在IP102数据集上达76.44%准确率，在D0数据集上达99.79%准确率。此外，通过大量评估与消融实验，全面验证了DeWi的优越性。源代码已公开于https://github.com/toannguyen1904/DeWi。
原文摘要 · Abstract (English)
Accurate insect pest recognition plays a critical role in agriculture. It is a challenging problem due to the intricate characteristics of insects. In this paper, we present DeWi, novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously improves several Convolutional Neural Networks in two perspectives: discrimination (by optimizing a triplet margin loss in a supervised training manner) and generalization (via data augmentation). From that, DeWi can learn discriminative and in-depth features of insect pests (deep) yet still generalize well to a large number of insect categories (wide). Experimental results show that DeWi achieves the highest performances on two insect pest classification benchmarks (76.44\% accuracy on the IP102 dataset and 99.79\% accuracy on the D0 dataset, respectively). In addition, extensive evaluations and ablation studies are conducted to thoroughly investigate our DeWi and demonstrate its superiority. Our source code is available at https://github.com/toannguyen1904/DeWi.
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