A feasibility Study of Detecting Chironomidae Larva in Water Treatment Filtration Processes using ResNet-based Image Recognition Deep Learning

There is a possibility that Chironomidae Larva may appear in sand and activated carbon filters during drinking water treatment. This study was conducted to determine whether the presence or absence of larva that may appear in filters through image data analysis. Image data were created for cases with and without larva background with interference materials such as sand and activated carbon granules used in the actual water treatment process. We used ResNet, one of the image classification deep learning models, and verified and evaluated its accuracy. Among the 12 models, the top three models with high TPR were ResNet50 No-pretrained LR=0.1, ResNet18 No-pretrained LR=0.001, and ResNet18 No-pretrained LR=0.01. Models trained by only obtaining the structure of the ResNet model without pre-training showed higher accuracy and superior performance. Among the models with the highest accuracy, ResNet50 No-pretrained LR=0.1 had a large TPR value. However, FPR also showed a large value. Therefore, it could not be suitable for judging the presence or absence of larva in the water treatment process.When comparing ResNet18 No-pretrained LR=0.001 and ResNet18 No-pretrained LR=0.01, which had the second and third highest TPRs, the ResNet18 No-pretrained LR=0.01 model had the highest Accuracy and F1 Score.