Recognition of gangues from color images using convolutional neural networks with attention mechanism

Accurate identification of coals and gangues from color images can reduce the environmental damage caused by the mining industry. Coals and gangues are prone to the problem of "different objects with the same spectrum" in color images. This problem makes it difficult for convolutional neural networks to focus on discriminative fea-tures of color images, which makes it difficult to identify coals and gangues. To more accurately identify coals and gangues from color images, a novel attention mechanism inspired by the human visual system is proposed and named Patch Attention. Patch Attention consists of a Patch Stem module and a patch channel attention module. The Patch Stem module reshapes the image into a sequence of patches and performs feature extraction using convolution to locate discriminative features. The patch channel attention module further improves the feature response of discriminative regions in the image patch so that subsequent convolutional layers focus on these regions. The recognition accuracy of the model improved with the Patch Stem module can be improved by 2.54%, and the patch channel attention module inserted in the Patch Stem module can increase the recognition accuracy of the model by 0.8%. Patch Attention enables the recognition accuracy of convolutional neural net-works to reach up to 99.37%. The Patch Stem module and patch channel attention module can significantly improve the recognition accuracy of coals and gangues from color images with convolutional neural networks.