Yolo Deep Model for Pallet Recognition Contribution into Industrial Area

Recent object detection developments have been largely influenced by advances in the field of deep learning. In this context, we have developed a pallet detection model integrated into a forklift robot in order to optimize the picking process in storage and logistics environments in industrial setti...

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Bibliographic Details
Published in2025 4th International Conference on Computing and Information Technology (ICCIT) pp. 502 - 506
Main Authors Ammar, Anis, Ghozzi, Rim, Sghaier, Souhir, Aziz, Gassab, Souani, Chokri
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.04.2025
Subjects
Accuracy
Deeplearning
Logistics
object recognition
Optimization
Pallets
Performance evaluation
RCNN
Real-time systems
Robustness
Service robots
Solids
YOLO
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DOI10.1109/ICCIT63348.2025.10989317

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Summary:Recent object detection developments have been largely influenced by advances in the field of deep learning. In this context, we have developed a pallet detection model integrated into a forklift robot in order to optimize the picking process in storage and logistics environments in industrial settings. The project based on the architecture of the YOLO (You Only Look Once) model to improve pallet recognition and estimation of position and distance allowing robots to autonomously locate and pick pallets. This model stands out for its ability to provide fast and accurate object detection in high-resolution images, making them particularly suitable for real-time applications such as robotics and logistics.
DOI:10.1109/ICCIT63348.2025.10989317