Machine Learning based Pallets Detection and Tracking in AGVs
This work addresses pallet handling efficiency in manufacturing and distribution, but it appears incremental as it builds on existing deep learning methods with optimizations.
The paper tackled pallet detection and tracking for automated guided vehicles (AGVs) using a deep learning architecture, achieving a 25% reduction in error rate, 28.5% reduction in false negative rate, and 20% reduction in training time.
The use of automated guided vehicles (AGVs) has played a pivotal role in manufacturing and distribution operations, providing reliable and efficient product handling. In this project, we constructed a deep learning-based pallets detection and tracking architecture for pallets detection and position tracking. By using data preprocessing and augmentation techniques and experiment with hyperparameter tuning, we achieved the result with 25% reduction of error rate, 28.5% reduction of false negative rate, and 20% reduction of training time.