# Feasible Applicability of Deep Learning for Solid Detection in Concrete Wastewater: An Evaluation
## 3 Discussion
### Deep Learning Applications in Solutions  
Now that a tiny defect or difference can be accurately detected, research into solution concentration detection with image processing and recognition has been widely conducted. [PERSON] et al. [21] carried out experiments to inspect the tungsten concentration in a solution via online image processing. Color features were extracted to train an LS-SVM model by building up a mathematical relationship between the tungsten concentration and color parameters for future predictions. The results showed that the intelligent and human-exclusive method technologically meets the industrial requirements, and replaces the traditional human-inclusive tungsten concentration analysis. Moreover, its accuracy and efficiency confirms this method's potential for future applications in other fields and in the solution concentration inspection industry. [PERSON] et al. [22] further detected the copper ion concentration in rice-field irrigation water based on image recognition by comparing the color changes of a test paper and the targeted samples in the images. The extraction of the gray-scale eigenvalues of the test paper assisted them in establishing and comparing four detection models, and they logarithmically regressed the optimal model for the test. Furthermore, it was found that the correlation coefficients of the training set and the prediction set were 0.9438 and 0.9191, and the root mean square errors were 19.9563 mg/L and 9.7889 mg/L, with a relative error below 8%. The results were practically proved to monitor the quality of irrigation water in paddy fields. Consequently, this deep learning technique was found to thoroughly replace the manual operations and facilitated the in-time inspections for ion concentration. Moreover, [PERSON] et al. [23] detected the concentration of a single-component dye solution using image recognition. The schematic experimental setup is shown in Figure 2a,b. Low-alkali yellow dyes were used to achieve the image samples for recognition. By comparing the maximum and average relative errors, the model with a blue light source was found to possess a brightness level of 50, which was the maximum prediction for the concentration of the solution, reaching a maximum relative error of 5.20% and an average relative error if 3.35%. Moreover, the simple structure and fast detection speed of the experimental equipment facilitated the utilization of this advanced technique of solution concentration detection. [PERSON] et al. [24] applied the OpenCV technique to reduce the quantity of sample images and improve the efficiency of the detection of color solutions. OpenCV exploited the gradient descent of the image brightness to precisely determine the major features of the image samples [25]. In experiments, the color features were extracted and used to predict the sample images acquired from the color solutions. The results demonstrated that the detected solution concentration extraordinarily matched the features extracted, with an allowable error of \(\pm\)5% for the recognition. The research on solution detection indicates that deep learning techniques can accurately predict input images, although the research objectives are metal elements, ions, and solution colors. Therefore, deep learning applications for the concentration detection in solutions provide a methodology for the detection of solid content in suspending liquids and facilitate these extended practices in the liquid detection industry. With its successful applications for detecting the solution concentration, [PERSON] et al. [26] exploited the image processing technique to detect the solid particles in mineral water. Compared with the low-efficient, time-consuming, and labor-intensive manual inspection method, the sample images from the image recognition technique were automatically acquired, analyzed, and matched with the pre-obtained images to realize target identifying, quantity counting, and size detecting for the suspending particles in the mineral water with a higher efficiency.