Improved CBAM Rock Image Classification Based on Xception
Tingting Zhang, Shuyan Ren, Hailong Duan, Shuoyi Wen · Frontiers in artificial intelligence and applications · 2023
To address the problems of traditional machine learning algorithms in rock recognition which are time-consuming, labor-intensive and low accuracy, a migration learning method using network weights for feature learning based on Xception network structure and incorporating an improved convolutional attention module (CBAM) to strengthen the important features of rocks, as well as adding a feature map perturbation structure to enhance the generalization ability of the model is proposed. Six types of manually collected rock sample images from five provinces, including Fujian and Zhejiang, are selected to construct the rock dataset and conduct experiments, and the learning rate is set at 0.001. Experiments show that the precision of the proposed method is 98.9% with 0.05 loss, which is higher than other mainstream recognition models with higher recognition accuracy and lower loss.