An artificial intelligence image-based approach for colloid detection in saturated porous media

Colloids in saturated porous media, such as soil and aquifers, play a critical role in the transport of nutrients, pollutants, and microorganisms. Their movement can influence the quality of groundwater and the effectiveness of filtration systems. Detecting colloids in these environments is essential for understanding contaminant spread, predicting soil and groundwater behavior, and managing water resources. Accurate detection helps in designing remediation strategies and ensures the safe use of natural resources, particularly in environmental engineering and hydrogeology. In this paper, we apply an artificial intelligence approach with the help of deep learning to detect colloids, which is a prerequisite for subsequent steps in porous media research. Since colloids are tiny particles and do not have enough information to identify, firstly we use an image processing technique called the dilation operation to improve distinguishing features of colloids for the detection process. This operation leads to achieving more accurate results for the detection of tiny colloids. Then, we propose a lightweight deep convolutional neural network to detect colloids automatically without the requirement for manual analysis. In our experiments, Precision, Recall, F-measure, and TCR metrics are employed for assessment. The experimental results show the efficiency and effectiveness of the proposed approach compared to six image processing methods in the detection process of colloids.