Towards Fine-Grained Sidewalk Accessibility Assessment with Deep Learning: Initial Benchmarks and an Open Dataset

We examine the feasibility of using deep learning to infer 33 classes of sidewalk accessibility conditions in pre-cropped streetscape images, including bumpy, brick/cobblestone, cracks, height difference (uplifts), narrow, uneven/slanted, pole, and sign. We present two experiments: first, a comparison between two state-of-the-art computer vision models, Meta’s DINOv2 and OpenAI’s CLIP-ViT, on a cleaned dataset of ∼ 24k images; second, an examination of a larger but noisier crowdsourced dataset (∼ 87k images) on the best performing model from Experiment 1. Though preliminary, Experiment 1 shows that certain sidewalk conditions can be identified with high precision and recall, such as missing tactile warnings on curb ramps and grass grown on sidewalks, while Experiment 2 demonstrates that larger but noisier training data can have a detrimental effect on performance. We contribute an open dataset and classification benchmarks to advance this important area.