Edge-cloud collaborative fabric defect detection based on industrial internet architecture

Abstract

Aiming to improve the adaptability of fabric defect detection, this paper proposes an “edge-cloud” collaborative fabric defect detection architecture that contains edge layer, platform layer, and application layer. In the edge layer, the fabric defect detection machine is able to realize the collection and detection of fabric images data. In the platform layer, the cloud platform that integrates memory computing, parallel storage, and a relational library is designed to realize the efficient storage and analysis of fabric data. In the application layer, a deep learning fabric defect detection algorithm is designed to recognize the defect patterns. The interaction between the cloud platform and the detection device is designed to adaptively adjust the detection algorithm. The closed-loop optimization is achieved by implementing “edge-cloud” architecture that the fabric pictures are captured and analyzed for fast detection algorithm in edge devices. The captured data is stored and monitored by the cloud platform. The cloud platform adjusts the edge detection algorithm by transfer learning, which can adapt to the changing environment. A case study illustrates that the proposed edge-cloud collaborative fabric defect detection can achieve better dynamic adaptability.