CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

Nowadays there is an increasing need for using artificial intelligence techniques in image-based documentation and survey in archeology, architecture or civil engineering applications. Brick segmentation is an important initial step in the documentation and analysis of masonry wall images. However, due to the heterogeneous material, size, shape and arrangement of the bricks, it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches. In this paper, we propose a new technique which combines the strength of deep learning for brick seed localization, and the Watershed algorithm for accurate instance segmentation. More specifically, we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process, which provides as output the accurate contours of the individual bricks, and also separates them from the mortar regions. For training the network and evaluating our results, we created a new test dataset which consist of 162 hand-labeled images of various wall categories. Quantitative evaluation is provided both at instance and at pixel level, and the results are compared to two reference methods proposed for wall delineation, and to a morphology based brick segmentation approach. The experimental results showed the advantages of the proposed U-Net markered Watershed method, providing average F1-scores above 80%.