Jan Zizka et al. (Eds) : CCSEIT, AIAP, DMDB, MoWiN, CoSIT, CRIS, SIGL, ICBB, CNSA-2016 pp. 91–99, 2016. © CS & IT-CSCP 2016 DOI : 10.5121/csit.2016.60608                 Luca Petricca *1 , Tomas Moss 2 , Gonzalo Figueroa 2 and Stian Broen 1 1 Broentech Solutions A.S., Horten, Norway *lucap@broentech.no 2 Orbiton A.S. Horten, Norway, info@orbiton.no ABSTRACT 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. KEYWORDS Deep Learning; Artificial Intelligence; Computer Vision; Caffe Framework; Rust Detection. 1. INTRODUCTION Bridge inspection is one important operation that must be performed periodically by public road administrations or similar entities. Inspections are often carried out manually, sometimes in hazardous conditions. Furthermore, such a process may be very expensive and time consuming. Researchers [1, 2] have put a lot of effort into trying to optimize such costly processes by using robots capable of carrying out automatic bridge maintenance, reducing the need for human operators. However such a solution is still very expensive to develop and carry out. Recently companies such as Orbiton AS have started providing bridge inspection services using drones (multicopters) with high resolution cameras. These are able to perform and inspect bridges in many adverse conditions, such as with a bridge collapse[3], and/or inspection of the underside of elevated bridges. The videos and images acquired with this method are first stored and then subsequently reviewed manually by bridge administration engineers, who decide which actions are needed. Even though this sort of automation provides clear advantages, it is still very time consuming, since a physical person must sit and watch hours and hours of acquired video and images. Moreover, the problem with this approach is twofold. Not only are man-hours an issue