基于深度学习的图像分类算法框架研究

Framework of Image Classification Algorithm Based on Deep Learning

目的提高图像分类精度是实现自动化生产的基础,提出一种更加准确的图像分类方法,使自动化包装和生产更加高效。方法基于ResNeSt特征图组的思想,通过引入通道域和空间域注意力机制,并将自适应卷积核思想和Gem池化引入空间域注意力模块,从而使网络在空间域注意力机制中能够对不同图片使用不同的感受野使其关注更重要的部分,提出一种具有通道域和空间域注意力机制,且具有很好移植性的图像分类网络模型结构。结果文中方法提高了图像分类准确度,在ImageNet数据集上,top-1准确度为81.39%。结论文中提出的ResNeSkt算法框架优于目前的主流图像分类方法,同时网络整体结构具有很好的移植性,可以作为图像检测、语义分割等其他图像研究领域的主干网络。

Improving the accuracy of image classification is the basis of automatic production.The work aims to proposes a more accurate image classification method to make automatic packaging and production more efficient.Based on the idea of ResNeSt feature graph group,by introducing the channel domain and spatial domain attention mechanism and introducing the idea of adaptive convolution kernel and gem pooling into the spatial domain attention module,the network could use different sensory fields for different pictures in the spatial domain attention mechanism to focus on more important parts.An image classification network model structure with channel domain and spatial domain attention mechanism and good portability was proposed.This method improved the accuracy of image classification.On ImageNet data set,the accuracy of top-1 was 81.39%.The ResNeSkt algorithm framework proposed in this paper is superior to the current mainstream image classification methods.At the same time,the overall network structure has good portability,and can be used as the backbone network in other image research fields such as image detection and semantic segmentation.