Learning-Based Vision Systems for Semi-Autonomous Forklift Operation in Industrial Warehouse Environments

TL;DR

This paper develops a vision-based system using YOLO architectures for pallet detection in warehouses, enabling cost-effective and accurate forklift automation through optimized perception modules.

Contribution

It introduces a novel pallet and pallet hole detection framework using YOLO models with hyperparameter tuning and spatial post-processing for warehouse automation.

Findings

YOLOv8 achieves high detection accuracy for pallets and holes.

YOLOv11 with optimization offers superior precision and stability.

The system demonstrates feasibility for low-cost, scalable warehouse automation.

Abstract

The automation of material handling in warehouses increasingly relies on robust, low cost perception systems for forklifts and Automated Guided Vehicles (AGVs). This work presents a vision based framework for pallet and pallet hole detection and mapping using a single standard camera. We utilized YOLOv8 and YOLOv11 architectures, enhanced through Optuna driven hyperparameter optimization and spatial post processing. An innovative pallet hole mapping module converts the detections into actionable spatial representations, enabling accurate pallet and pallet hole association for forklift operation. Experiments on a custom dataset augmented with real warehouse imagery show that YOLOv8 achieves high pallet and pallet hole detection accuracy, while YOLOv11, particularly under optimized configurations, offers superior precision and stable convergence. The results demonstrate the feasibility of a cost effective, retrofittable visual perception module for forklifts. This study proposes a scalable approach to advancing warehouse automation, promoting safer, economical, and intelligent logistics operations.