基于深度学习网络的煤矸石分类研究

Study on Classification of Coal Gangue Based on Deep Learning Network

针对传统Vgg网络对煤矸石分类任务训练速度慢的问题,本文使用两种深度学习网络对煤矸石样本图像进行分类测试,并与Vgg网络进行性能对比,发现相比传统的Vgg深度学习网络,轻量化深度学习网络的模型训练速度明显加快,在保证分类精度基本不下降的前提下模型训练时间可减少75%。

In view of the slow training speed of traditional Vgg network for coal gangue classification task,this paper uses two deep learning networks to classify and test coal gangue sample images,and compares their performance with Vgg network.It is found that compared with traditional Vgg deep learning network,the model training speed of lightweight deep learning network is obviously accelerated,and the model training time can be reduced by 75%while the classification accuracy is basically not reduced.