RasPiDets: A Quasi-Real-Time Defect Detection Method With End-Edge-Cloud Collaboration

Abstract

In this article, we focus on the problem of product defect detection (PDD) in air conditioner (AC) manufacturing. The challenges are twofold: first, the scale of the objects undergoes significant variations, thereby increasing the difficulty of detection; second, the computing power of terminals is limited, and it may be difficult to meet the quasi-real-time detection requirements. Therefore, to improve detection accuracy and speed, a lightweight object detection model tailored for deployment on the compact wireless Raspberry Pi, is proposed: a deep cascaded U-shape network is presented to effectively capture both global context and local details of objects, which can reduce the feature redundancy and the number of the model parameters. An adaptive multiscale squeeze-and-excitation is designed for feature reuse and fusion, enhancing both detection accuracy and efficiency. Then, to meet the quasi-real-time detection demands and better utilize end-edge-cloud resources in industrial internet of things (IIoTs), an actor–critic-based dynamic offloading (ACDO) algorithm is proposed to minimize the long-term cumulative time of task detection. ACDO utilizes the elapsed time as the reward to directly optimize the mixed variables of the task to achieve efficient offloading. The proposed methods are verified at an AC manufacturing line, which demonstrates accurate and quasi-real-time defect detection, achieving a 64% reduction in runtime and a 1.2% improvement in average mean average precision. In addition, we publish two PDD datasets to accelerate the related research.