Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery
| Autor: | Esther Jang, Jon E. Froehlich, Galen Weld, Aileen Zeng, Anthony Li, Kurtis Heimerl |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Training set
Artificial neural network business.industry Deep learning 05 social sciences 02 engineering and technology Pedestrian Machine learning computer.software_genre Residual neural network Application areas 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Generalizability theory Artificial intelligence business computer 050107 human factors |
| Zdroj: | ASSETS |
| Popis: | Recent work has applied machine learning methods to automatically find and/or assess pedestrian infrastructure in online map imagery (e.g., satellite photos, streetscape panoramas). While promising, these methods have been limited by two interrelated issues: small training sets and the choice of machine learning model. In this paper, aided by the recently released Project Sidewalk dataset of 300,000+ image-based sidewalk accessibility labels, we present the first examination of deep learning to automatically assess sidewalks in Google Street View (GSV) panoramas. Specifically, we investigate two application areas: automatically validating crowdsourced labels and automatically labeling sidewalk accessibility issues. For both tasks, we introduce and use a residual neural network (ResNet) modified to support both image and non-image (contextual) features (e.g., geography). We present an analysis of performance, the effect of our non-image features and training set size, and cross-city generalizability. Our results significantly improve on prior automated methods and, in some cases, meet or exceed human labeling performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|