RGBD-based method for segmenting apparent pores within bridge towers

Abstract

Abstract As a crucial technology in computer vision, image semantic segmentation is extensively applied to tasks such as detecting apparent defects in concrete, identifying structural cracks, and interpreting facility scenes within infrastructure settings. Challenges such as uneven lighting inside bridge towers and the similarity in color and texture between concrete pore structures and their surrounding areas frequently result in lower segmentation accuracy. This article introduces a multimodal semantic segmentation model incorporating depth information to tackle these challenges. By integrating depth and RGB images as inputs, the model constructs an interactive space and utilizes a cross-attention mechanism along with global context for guided learning, thus enabling precise feature extraction and segmentation. The experimental results demonstrate that this segmentation network excels on a custom-made concrete pore dataset, with a precision rate of 90.88%, recall rate of 87.48%, intersection over union (IoU) of 80.42%, and F1 and Dice coefficient of 89.10% and 89.15%, respectively. These outcomes affirm the network’s effectiveness in segmenting apparent defects in concrete and offer technical support and solutions for image semantic segmentation tasks in settings like bridge towers.