Advancing Automated Reporting in Manufacturing With Dual-Channel GCN and CNN Architectures

Abstract

The automation of reporting in the manufacturing sector, enabled by advanced sensing technologies and sophisticated software, offers considerable promise for enhancing operational efficiency and profitability. Despite substantial economic benefits, fully realizing automated reporting remains hindered by critical challenges in human action recognition. These obstacles include visual occlusions in dynamic environments, heavy reliance on large-scale labeled video datasets, requirements for real-time activity identification, and the complexity of recognizing sequential and interdependent tasks typical in manufacturing settings. This article introduces a novel CNN-based framework specifically designed to overcome these challenges, thereby enhancing automated reporting capabilities in manufacturing. Our proposed method incorporates innovative geometric convolution layers, significantly improving the accuracy of motion identification and posture estimation under challenging conditions. To reduce dependence on extensive labeled datasets, we integrate a self-supervised learning strategy, leveraging unlabeled video data effectively. Moreover, we propose a robust dual-channel graph convolutional network that concurrently addresses multitask action recognition and prediction, effectively capturing complex sequential activities. Collectively, these contributions equip manufacturers with the tools necessary for precise, real-time monitoring of worker actions, enabling optimized operations, enhanced worker safety, and ultimately, improved profitability.