Research on efficient classification algorithm for coal and gangue based on improved MobilenetV3-small

ABSTRACT

Aiming at the problems of insufficient attention to spatial information, long classification training time, and high model complexity in current gangue image classification algorithms, a lightweight fast recognition model for gangue based on improved MobileNetV3-small is proposed. First, we optimize the feature extraction part by introducing the CeLU activation function to alleviate the neuron death and gradient vanishing problems, thus improving the performance of the model. Second, Bneck is further optimized using an improved Triplet Attention Module to achieve almost parameter-free spatial and channel dimension interactions, which drastically reduces the complexity of the model. Finally, we construct a new Efficient Last Stage structure, which improves the model performance while successfully reducing the computation and model size by replacing the traditional convolution with Ghost Module and introducing Mix-pool as a pooling layer. In addition, the effectiveness of each component was fully demonstrated through ablation experiments, visualization analysis, and comparative experiments. The experimental results show that training the homemade gangue dataset using the improved MobilenetV3-small model improves Accuracy and F1-Score on the validation set by 0.48% and 0.42%, respectively, compared to the original network, and in terms of model complexity, we can see that FLOPs, number of parameters, and Weight size are reduced by 2.8%, 33.0%, and 32.3%, respectively, and FPS reaches 28 frames per second. The improved algorithm greatly reduces the model complexity and decreases the dependence on hardware devices while improving the classification accuracy and anti-interference capability. This provides an important reference basis for deploying automated underground coal gangue sorting, which helps to realize intelligent integrated mining.

KEYWORDS:

• Coal and gangue
• image classification
• mobilenetV3-small
• lightweight
• attention mechanism

Acknowledgement

We thank the editor-in-chief and reviewers for their valuable comments on this paper, and the other authors of this article for their help with the article.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data availability Statement

The authors declare that the datasets in this article are original datasets, and readers can send information to the corresponding author’s e-mail address of the article if needed.

Additional information

Funding

This research was funded by Natural Science Research Project of Anhui Educational Committee (No. KJ2021A0427); Postgraduate Innovation Fund of Anhui University of Science and Technology (No. 2023cx2084).