基于改进ShuffleNetV2网络的岩石图像识别

Rock Image Recognition Based on Improved ShuffleNetV2 Network

由于基于传统深度学习的岩石图像识别算法模型比较繁琐,而且应用于移动终端等需要一定的计算能力,因此很难实现对岩石类型的实时准确判别。为此,以ShuffleNetV2网络为基础,插入通道连接注意力机制ECA(Efficient Channel Attention)模块,使用Mish激活函数代替ReLU激活函数并引入轻量级网络部件中的深度可分离卷积。将该方法用于岩石图像识别,实验结果表明,改进后的算法结构简单,同时具有轻量化的特点,其识别精度达到94.74%,可在移动终端等有限资源环境下应用。

The rock image recognition algorithm model based on traditional deep learning is cumbersome and requires certain computing power when it is applied to mobile terminals,so it is difficult to realize real-time and accurate identification of rock types.Based on the ShuffleNetV2 network,we insert the ECA(Efficient Channel Attention)module of the channel connection attention mechanism,use the Mish activation function to replace the ReLU activation function,and introduce the depthwise separable convolution in the lightweight network components.Experiments are performed on rock images with this method.Experiments show that the recognition accuracy of the algorithm reaches 94.74%.The improved algorithm structure is not complex and maintains the characteristics of lightweight,which lays a foundation for its application in limited resource environments such as mobile terminals.