Optimized Pallet Localization Using RGB-D Camera and Deep Learning Models

This paper presents an optimization and multiobjective evaluation of deep learning (DL) models to improve pallet localization with an RGB-D camera in scenarios of forklift insertion. To this end, we experimentally evaluate three distinct DL models: Detectron2 for combined detection and segmentation, YoloV5 for detection only, and a combination of YoloV5 and UNet for detection and segmentation. Through the automatic hyperparameter optimization process on a custom dataset, 30 configurations were selected, each demonstrating a unique trade-off between precision and speed. Out of these, three Pareto optimal models were chosen for a more detailed analysis, considering inference speed and localization errors along each orthogonal axis. The results suggest that the proposed hybrid model combining YoloV5 and UNet exhibited a promising balance of speed and accuracy, making it suitable for real-time applications. Finally, the proposed model is demonstrated for a fork insertion task in a new environment.