Improving efficiency and accuracy of levee hazard detection with deep learning

Abstract

This paper presents a model framework based on a deep learning algorithm using Faster R-CNN to improve the accuracy and efficiency of detecting long-distance levee hazards. The proposed method aims to assist technicians in identifying hazards in resistivity inversion profiles, thereby reducing the manual workload and extracting valuable information from a large volume of data using deep learning techniques. To this end, we constructed a dataset of levee hazards using inversion images of electrical resistivity imaging data obtained from numerical simulations, indoor experiments, and actual levee hazards. Subsequently, we utilized image augmentation techniques to expand the dataset and enhance the model's performance, resulting in a total of 2000 labeled hazard images. The network was then trained from scratch by setting the hyperparameters. the trained weight parameters and network architecture were saved for future use. The model's performance was evaluated using standard evaluation metrics, including precision, recall, F1 score, and average precision (AP). The proposed model demonstrated robustness and adaptability, achieving high precision and accuracy with an AP value of up to 100%. Furthermore, we validated the method using three cases, and the results indicated its superiority over traditional manual identification-based or outlier analysis detection of levee hazards in practical scenarios. This study emphasizes the significant advantages of deep learning techniques over the traditional methods, including improved accuracy, automation, speed, and scalability. It suggests that intelligent detection based on deep learning can accurately identify and locate hazards in levees.