A Computational Intelligence Framework for Staircase Detection and Reconstruction Using Depth-Based Deep Learning

This study presents a computational intelligence-based method for robust detection and reconstruction of staircases in complex environments. The proposed framework combines point cloud processing with deep learning-based object detection to address challenges related to lighting variability, occlusion, and computational cost. A central component is the generation of Orthogonal Grayscale (OGS) images — 2.5D elevation maps encoded as grayscale images — which preserve spatial structure while enabling the application of efficient 2D convolutional neural networks. A customized YOLOv5-OBB model is trained on a newly collected dataset to localize and classify staircases under various conditions, including complete darkness. The detected regions are then analyzed using grayscale adaptive histograms to estimate stair height and count, followed by 3D reconstruction through bounding volume methods. Experiments demonstrate detection accuracy of 98.8% for upstairs and 94.5% for downstairs scenarios, with real-time inference performance. The integration of neural vision, elevation encoding, and spatial reconstruction exemplifies a hybrid computational intelligence system applicable to robotics, assistive navigation, and autonomous inspection tasks.