  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.
