**4. Conclusions**

To improve the recognition accuracy of coal gangue images, a coal gangue image recognition method based on the BP neural network and ASGS-CWOA (ASGS-CWOA-BP) was proposed, which makes two key contributions. Firstly, a new feature extraction method regarding the unique features of coal and gangue images is proposed. Additionally, a method using ASGS-CWOA to optimize the parameters of the BP neural network was

introduced to address the issue of low accuracy in coal gangue image recognition, and a BP neural network with a simple structure and reduced computational consumption was designed. The theoretical research and experimental results revealed that compared with GA-BP, PSO-BP, LWCA-BP, BP and RF, ASGS-CWOA-BP had the best classification accuracy and high robustness under the same conditions.

Compared with the five other algorithms, ASGS-CWOA-BP performed well in most cases on the training set and test set, and its best classification accuracy on the training set was 95.47% while that on the test set was 94.37%, as shown in Table 4 and Figure 6a. It should be emphasized that this was achieved under extremely limited conditions as follows: (1) the structure of the BP-based recognition network was extremely simple (only six-dimensional feature vectors were required in the input layer and only four nodes in the hidden layer), and (2) the number of samples was very small (only 358 coal gangue image samples). These extremely limited conditions greatly reduced the amount of calculation, and the GPU was not used from beginning to end; therefore, all simulation experiments can be implemented only on a laptop with ordinary performance, as detailed above, which shows that the new method proposed in this article has superior performance. In fact, the recognition model trained by this method is quite suitable for use in mobile portable coal gangue image recognition equipment with weak computing power and low energy consumption.

**Table 4.** Statistical analysis of classification accuracy.


However, what needs to be remembered is that the most popular image recognition model based on deep learning has higher and better recognition or classification accuracy and has been studied by a considerable number of scholars. Unfortunately, the network of this technology is complex (with many levels and a large amount of calculation) and often requires a large number of image samples. On the contrary, this is exactly the advantage of the method proposed in this article.

Our future work is to continue to improve the performance of the wolf pack optimization algorithm and to apply it to optimize a more complex BP-based recognition network to increase the feature dimensions of the extracted coal gangue images and increase the number of samples to improve the classification accuracy of coal gangue images.

**Author Contributions:** D.W. conceived the algorithm framework and wrote the article; J.N. and D.W. performed the program experiments; J.N. and T.D. contributed the data. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by National Key Research and Development Program of China (grant no. SQ2018YFC060172).

**Acknowledgments:** The authors are grateful to their peer experts for the full support of this paper and thank Zhuhai Xinhe Technology Co., Ltd. and China University of Mining and Technology-Beijing for providing the necessary scientific research environment, as well as special thanks to Beijing Union University for its support of scientific research funds.

**Conflicts of Interest:** The authors declare no conflict of interest.
