Research on staircase image detection technology

As a typical environmental target, the staircase area needs to have the function of detecting and recognizing stairs, whether it is a robot system that can climb stairs independently or a software system that can remind visually impaired people to pay attention to obstacles. A staircase region detection algorithm with dual attention mechanism and bidirectional cross scale feature fusion is proposed to address the issue of false detections and missed detections in current staircase images due to their similar shapes to areas such as tiles, tables, and chairs in reality. Firstly, a dual attention mechanism was introduced into the backbone network of Yolov5s to extract semantic dependencies between different dimensions, eliminate indirect correspondence between channels and weights, and improve detection accuracy by focusing on differences in similar samples. Secondly, a weighted bidirectional feature pyramid network structure is adopted in the feature pyramid to achieve efficient bidirectional cross scale connections and weighted feature fusion, enhancing the network's feature extraction ability. The experimental results show that compared with the original algorithm, the improved algorithm has mAP@0.5 Improved by 1.3%, with higher detection accuracy. In addition, we also conducted experiments on staircase data collected under different lighting conditions to verify the effectiveness of the algorithm.