Exploring Corrosion Detection: Deep Learning and Ensemble Approaches Analysis

Corrosion is a widespread challenge in industrial and urban settings, silently causing damage to buildings, pipes, and machinery, leading to significant financial losses, safety risks, product quality issues, and operational inefficiencies. The goal of this research is to improve safety, quality, and efficiency in various areas by going beyond simple data analysis and statistics. It assesses different deep learning techniques and shows that the Conventional Convolutional Neural Network (CNN) is the best for corrosion detection. In order to further improve precision, the study combines a Random Forest classifier with a Conventional CNN model, leading to notable gains in accuracy and F1 score. The study also investigates the usage of Extra Trees with Conventional CNN, demonstrating the benefit of Bootstrap Aggregation in improving the accuracy of corrosion detection. These findings strengthen endeavors aimed at preserving infrastructure by significantly aiding in the detection of corrosion, with potential applications in drone technology and web application development for early corrosion detection.