Efficient and Robust Pallet Detection Using RGB-D Sensors and Synthetic Data Augmentation

This paper proposes a deep learning-based approach for pallet detection and pose estimation using RGB-D sensors, addressing key challenges in forklift-operated logistics and manufacturing environments. Existing research often faces limitations such as environmental constraints and data scarcity. To overcome these issues, our method combines synthetic data generation with Diffusion model-based data augmentation techniques, generating diverse pallet datasets with varying sizes and shapes using advanced 3D simulation tools. A modified YOLOv11 network is introduced to detect pallet bounding boxes and estimate the center and corner points of cuboids. The network is trained on the generated data and evaluated using real-world RGB-D data in real-time. The proposed approach significantly improves the precision of pallet detection and pose estimation in complex environments, contributing to logistics automation and offering broader implications for various industrial applications.