Luca Petricca¹, Tomas Moss², Gonzalo Figueroa² and Stian Broen¹,
¹Broentech Solutions A.S., Norway and ²Orbiton A.S. Horten, Norway

In this paper we present a comparison between standard computer vision techniques and Deep Learning approach for automatic metal corrosion (rust) detection. For the classic approach, a classification based on the number of pixels containing specific red components has been utilized. The code written in Python used OpenCV libraries to compute and categorize the images. For the Deep Learning approach, we chose Caffe, a powerful framework developed at “Berkeley Vision and Learning Center” (BVLC). The test has been performed by classifying images and calculating the total accuracy for the two different approaches.

Deep Learning; Artificial Intelligence; Computer Vision; Caffe Framework; Rust Detection.