关键词: 无人驾驶叉车；机器学习；YOLO；树莓派；叉车托盘

Abstract: In the logistics industry, while unmanned forklifts is gradually being applied. A very important feature of unmanned forklifts is the ability to detect the position of the pallet holes used for loading, so that the forklift can insert the pallets to transport the cargo. Machine learning algorithms are widely used to automatically detect the position of an object, such as the location of a hole. This paper describes the development of a deep learning approach to identifying the position of pallet holes using YOLO and Raspberry Pi. The model uses the YOLOv5 model, and the model is trained with a self-built pallet dataset, and the algorithm is deployed into an embedded device to achieve pallet hole recognition. The experimental results show that the recognition accuracy of pallet hole is around 88% while meeting the real-time performance requirements, which is low cost and high speed compared to the main methods currently used in the industry.

Key words: Unmanned forklift; Machine learning; YOLO; Raspberry Pi; Forklift pallet