CrosswalkNet: An Optimized Deep Learning Framework for Pedestrian Crosswalk Detection in Aerial Images with High-Performance Computing
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
With the increasing availability of aerial and satellite imagery, deep
learning presents significant potential for transportation asset management,
safety analysis, and urban planning. This study introduces CrosswalkNet, a
robust and efficient deep learning framework designed to detect various types
of pedestrian crosswalks from 15-cm resolution aerial images. CrosswalkNet
incorporates a novel detection approach that improves upon traditional object
detection strategies by utilizing oriented bounding boxes (OBB), enhancing
detection precision by accurately capturing crosswalks regardless of their
orientation. Several optimization techniques, including Convolutional Block
Attention, a dual-branch Spatial Pyramid Pooling-Fast module, and cosine
annealing, are implemented to maximize performance and efficiency. A
comprehensive dataset comprising over 23,000 annotated crosswalk instances is
utilized to train and validate the proposed framework. The best-performing
model achieves an impressive precision of 96.5% and a recall of 93.3% on aerial
imagery from Massachusetts, demonstrating its accuracy and effectiveness.
CrosswalkNet has also been successfully applied to datasets from New Hampshire,
Virginia, and Maine without transfer learning or fine-tuning, showcasing its
robustness and strong generalization capability. Additionally, the crosswalk
detection results, processed using High-Performance Computing (HPC) platforms
and provided in polygon shapefile format, have been shown to accelerate data
processing and detection, supporting real-time analysis for safety and mobility
applications. This integration offers policymakers, transportation engineers,
and urban planners an effective instrument to enhance pedestrian safety and
improve urban mobility.
