关键词: 岩石图像; , 有效通道注意力机制; , Mish 激活函数; , ShuffleNet 网络

Abstract: 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.

Key words: rock image; , efficient channel attention(ECA); , Mish activation function; , ShuffleNet network