3-D deep learning-based item classification for belt conveyors targeting packaging and logistics

In this study, we apply concepts taken from the fields of Artificial Intelligence (AI) and Industry 4.0 to a belt conveyor, a key tool in the packaging and logistics industries. Specifically, we present an item classification model built for belt conveyors, helping the conveyor control system to recognize items while minimizing its impact on the conveyor design and the movement of items. To that end, we followed a three-pronged approach. First, we converted a size measurement system into a 3-D shape reconstruction system by recycling a belt conveyor prototype developed in a previous study. Secondly, we transformed a scanned point cloud that varies in size, given the use of variable-length items, into a point cloud with a fixed size. Thirdly, we constructed three different end-to-end 3-D point cloud classification models, with the Dynamic Graph Convolutional Neural Network (DGCNN) model coming out on top when considering accuracy, response time, and training stability.