Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework

This papers focuses on symbol spotting on real-world digital architectural floor plans with a deep learning (DL)-based framework. Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. In this paper, we address all of the above issues by leveraging recent advances in DL and adapting an object detection framework based on the You-Only-Look-Once (YOLO) architecture. We propose a training strategy based on tiles, avoiding many issues particular to DL-based object detection networks related to the relative small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experiments on real-world floor plans demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity, even in the presence of heavy occlusion and clutter. Additional experiments on the public SESYD dataset confirm that our proposed approach can deal with various degradation and noise levels and outperforms other symbol spotting methods.

A Deep Learning-based Framework for the Detection of Schools of Herring in Echograms

Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.