Branchy Deep Learning Based Real-Time Defect Detection Under Edge-Cloud Fusion Architecture

Many machine-learning-based defect detection methods, especially deep learning-based approaches, have high requirements on computing power and network. They lead to time delay, high cost, and energy consumption when applied to deal with the massive data in an autonomous manufacturing enterprise. So efficient detection in the end-edge-cloud architecture is a good solution to overcome the above challenges. A branchy deep learning detection model with early exit ability of inference is proposed, in which the main branch is deployed on the cloud server and the side branches are on edge equipment. The proposed method quickly and effectively detects the category and location of the defect in printed circuit boards since partial computing task is offloaded to the edge nodes. A prototype system is implemented based on a computer as the cloud server and a Raspberry Pi as an edge node in order to verify the feasibility of the proposed method. The experiment result manifests high detection accuracy and fast computing speed.