基于双线性池化的实蝇分类注意力网络

Attention networks for fruit fly classification based on bilinear pooling

实蝇是国内外备受关注的检疫害虫,种类繁多。不同种类的实蝇外形大小相似,不易鉴别。此外,在实际应用中,鉴别实蝇的可用信息会受遮挡、视角、光影变幻等因素影响,导致实蝇自动识别工作难以进行。提出基于双线性池化的实蝇分类注意力网络,用于学习有效的实蝇鉴别特征。该网络由显著性特征模块和跨层双线性模块两个部分组成:显著性特征模块通过对不同卷积层进行滤波增强处理,实现特征增强;跨层双线性模块基于双线性池化融合特征,确定注意部位,挖掘判别特征。在具有自然环境背景的实蝇数据集上进行的实验表明,该方法效果较好,具有良好的实际应用前景。

Fruit fly is a kind of quarantine pest that attracts much attention at home and abroad.There are many kinds of fruit flies.Different kinds of fruit flies are similar in shape and size,which is difficult to identify.In addition,in practical applications,it is difficult to identify fruit flies due to the lack of information about shielding,view-point,changing light and shadow and other factors.This study proposes a bilinear pooled attention network for fruit fly classification to learn effective discriminant characteristics.The network is composed of two parts:saliency feature module and cross-layer bilinear feature module.Saliency feature module realizes feature enhancement by filtering enhancement processing of two different convolution layers.Cross-layer bilinear module is based on bilinear pooling fusion features,determines the attention location,and mines discriminant features.Experiments on fruit fly’s data set with natural environment background show that the method is effective and has good practical application prospect.