融合特征增强模块的小样本农业害虫识别

Few shot learning of agricultural pests classification fusion with enhanced feature model

摘要:

基于深度学习的图像识别技术在具体应用前必须先经过大量带标签样本的训练，然而在实际场景中目标域样本可能非常稀缺，小样本图像识别技术应运而生.为了提升小样本场景下的图像识别准确率，本文提出一个通用的两阶段训练模型以融合现行主流方法并增强其表现.首先，针对训练时不同害虫种类背景相似度过高的问题提出融合双注意力机制的特征加强模块；其次，针对小样本情况下预测可能产生的过拟合问题提出基于高斯分布的特征生成模块以提高泛化能力；最后，将三种典型小样本识别方法统一成两阶段训练模型以融入提出的方法.将该思路及改进首次应用于传统害虫分类数据集IP102，识别准确率可以在基准方法上取得2.11%到6.87%的提升.为了进一步验证本文方法的有效性，在小样本领域公开数据集MiniImagenet也进行了相应的实验，提升效果同样显著.

Abstract:

In order to achieve accurate image recognition in scenarios where the target domain samples are limited，such as agricultural pest Image recognition, few shot image classification methods have been developed as an extension of deep learning-based image classification .To further improve the accuracy in the few shot image classification, this paper proposes a general two-stage training model that integrates current mainstream methods and enhances their performance to improve the recognition accuracy in limited sample scenarios.Firstly, a feature enhancement module incorporating dual attention mechanism is proposed to solve the problem that the background similarity of different pest species is too high during training. Secondly, a feature generation module based on Gaussian distribution is proposed to solve the problem of overfitting that may occur in prediction in the case of a single sample. to improve the generalization ability. Finally, three typical few-shot recognition methods are unified into a two-stage training model to incorporate the proposed method. This idea and improvement are applied to the traditional pest classification dataset IP102 for the first time, and the recognition accuracy can be improved by 2.11% to 6.87% over the benchmark method. In order to further verify the effectiveness of the method in this paper, corresponding experiments were also carried out on the public dataset Mini-Imagenet in the field of few shot learning, the improvement effect is also significant.