LARGE SCALE SEMANTIC SEGMENTATION OF VIRTUAL ENVIRONMENTS TO FACILITATE CORROSION MANAGEMENT
- Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil
Keywords: Semantic Segmentation, Panoramic Imagery, Corrosion, deep learning
Abstract. This paper reports the results of a study that aims to develop semi-automatic methods for assessing the degree of corrosion in industrial plant. We evaluated two fully convolutional networks (U-Net and DeepLab v3 +) to segment corroded areas in panoramic images of offshore platforms. The experimental analysis was based on two datasets built for this study. The datasets comprise 9,112 2D images and 3,732 panoramic images. Both FCNs trained on 2D images were tested on 2D images and cubic projections of panoramic images. In addition to pointing out encouraging results, the experiments indicated that most prediction errors concentrated in corrosion defects with a small pixel area.