Abstract
The rapid growth of express packaging waste (EPW) is creating increasing challenges for urban waste management and high-quality resource recovery, while reliable automated sorting remains difficult because of heterogeneous materials, deformable packaging, random stacking, and frequent partial occlusion. This study develops a system-oriented RGB-D sensing framework to improve the reliability of EPW sorting in conveyor-belt recycling operations. A synchronized and spatially registered RGB-D paired dataset, MEPWaste, was constructed containing 4528 paired samples (9056 RGB/depth images) from 10 representative categories of inner and outer packaging under multiple levels of stacking and occlusion. Based on this dataset, a multimodal detection framework was designed to improve feature reliability within each modality, reinforce spatial consistency between modalities, and enhance detection stability in densely stacked scenes with fragmented visible evidence. The proposed framework achieved 89.1% mAP50 and 69.3% mAP50:95, exceeding the RGB-only model by 5.3 and 5.8 percentage points, respectively, and the RGB-D baseline by 2.7 and 3.4 percentage points, with recall reaching 85.1%. A closed-loop sensing–inference–execution pipeline integrating synchronized RGB-D acquisition, real-time detection, robotic grasping, and PLC-based control was further implemented to examine engineering feasibility. Overall, the study provides a practical technical pathway for improving EPW sorting reliability and supporting intelligent resource recovery in waste management systems, with potential applicability to green campus management scenarios characterized by high-frequency parcel consumption and concentrated packaging waste generation.