Beyond RGB-D: Quasi-Inverse Perspective Transformation Enables High-Precision Waste Segmentation in Noisy Construction Sites

Abstract

As a massive amount of waste generated by global construction activities, robots capable of automatically recycling construction waste have become effective tools for conserving natural resources. However, the complex environments of construction sites and the high diversity of waste materials pose challenges for robotic inspection, target recognition, and grasping. Achieving precise waste recognition is a prerequisite for stable grasping operations. In construction sites, complicated lighting conditions and cluttered backgrounds result in low feature contrast of waste materials in traditional RGB images, severely hindering recognition accuracy. To eliminate illumination interference, this study employs a depth camera to acquire scene disparity information and develops a Quasi-Inverse Perspective Transformation (QIPT) module, which enhances geometric contrast between objects and background by generating transformed disparity (TD) maps. Experimental validation within a single-modal fusion framework based on UNet demonstrates that the TD modality significantly outperforms RGB and raw disparity modalities in segmentation performance, achieving an improvement of 12.8% in the mIoU metric.