## 4 Discussion
### Experimental Environment and Datasets  
The hardware platform implemented in the experiment is as follows: the CPU is Inter(R) Core(TM) i9-9980 XE, 3.00 GHz, the GPU is NVIDIA GeForce RTX 2080 Ti, and RAM is 64 GB. The software runs on Windows 10 Professional Edition, Matlab2015a, Python 3.7 and Pytorch 1.9.0. The flow chart of the mineral flotation process is shown in Figure 11. The flotation process is divided into roughing, cleaning and scavenging, and cleaning is further divided into primary and secondary cleaning. The high-acid-leaching pulp first enters the roughing  
Figure 10: Structure of DenseNet-DT when DT is connected to different fully connected layers. separation process, and the overflow froth of roughing is sent to the two-level cleaning process. Then, the overflow froth of secondary cleaning is filtered through the compressor to obtain the final concentrate. The bottom pulp in the roughing process is discharged into the scavenging process to obtain tailings. The overflow froth of scavenging and the bottom pulp of two-level cleaning are returned to the roughing process for recycling. The camera was installed above the secondary cleaning tank to capture the froth images. The dataset in this study was collected at a metallurgical plant site. The data acquisition device consists of an RGB camera with a resolution of 1280 \(\times\) 960 and a 35 mm lens, a high-frequency light source, a cover hook to protect the camera from dust, acid fog and ambient light, and an optical fiber with a length of more than 200 m, which is used for signal communication to the industrial PC in the operating room. The camera is mounted vertically on the flotation cell to capture the flotation froth images at a rate of 15 frames/s. The dataset in this experiment consists of 750 mineral flotation images under four classes of working conditions (including 30 C1 images, 300 C2 images, 190 C3 images and 230 C4 images), and the ratio of the training set to test set is 8:2. Figure 12 shows the froth images corresponding to four typical working conditions in the mineral flotation process. 1. The image corresponding to working condition C1 has less visible froth and higher water content in the froth. Numerous white bright spots are reflected on the froth surface, and the concentrate grade is typically below 50%, as shown in Figure 12a. 2. The image corresponding to working condition C2 has more froth, and the size of the froth is not uniform. Small froth is mixed with some larger froth, the froth color is lighter, and the texture is more uniform. The concentrate grade is usually above 50% and below 60%, as shown in Figure 12b. Figure 11: The flow chart of mineral flotation process. 3. The image corresponding to working condition C3 has a uniform froth size and a relatively small size. The color is darker, and the texture is rougher. The concentrate grade is relatively normal, usually above 60% and below 70%, as shown in Figure 11(c). 4. The image corresponding to working condition C4 has a smaller froth size. The froth color is very dark, and the texture is rough. The flotation working condition is better, and the concentrate grade is normally above 70%, as shown in Figure 11(d). To quantify the performance of the proposed and other methods in this study, four evaluation metrics were used, including the accuracy rate, precision rate, recall rate and F1 score. The accuracy rate represents the percentage of the total samples that were predicted correctly, and the formula is as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\% \tag{21}\]  
where TP is the true positive class, TN is the true negative class, FP is the false positive class, and FN is the false negative class.