A Low Cost Cross-Platform Video/Image Process Framework Empowers Heterogeneous Edge Application

Recently, video/image intelligent analytics has been widely used in industrial Artificial Intelligence (AI) applications, such as defect detection, face recognition, and security monitoring. To provide better applicability and compatibility in such applications, the embedded AI models must be developed, compiled, and deployed under different development frameworks, such as cuDNN, RKNN, etc. Unfortunately, these frameworks are supported by various Graphic Processing Unit (GPU) hardware vendors, resulting in different model parameter structures and increased development costs. To address these issues, we propose LiGo , a low cost cross-platform video/image process framework, that simplifies and accelerates video intelligent processing in practical heterogeneous hardware systems. LiGo 1 provides video processing pipeline, cross-platform development environments, and unified model serving structures. We demonstrate LiGo's efficiency and flexibility in model generation and deployment through its use in supporting multiple real-world commercial industrial systems.