Learning-Based Vision Systems for Semi-Autonomous Forklift Operation in Industrial Warehouse Environments
This work addresses the problem of cost-effective perception for semi-autonomous forklifts in warehouses, though it is incremental as it builds on existing YOLO methods.
The paper tackled pallet and pallet hole detection for forklifts using a vision-based framework with YOLO architectures and hyperparameter optimization, achieving high accuracy and superior precision with YOLOv11 on a custom dataset.
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.