Edge-Intelligent Industrial Inspection: A GPU-Accelerated Multiscale CNN Framework for Real-Time Visual Quality Assessment

Abstract

The transition toward Industry 4.0 requires the integration of technologically viable hardware–software–intelligence solutions into existing industrial infrastructures to enable smart and autonomous production systems. Thus, Industry 4.0 enables techno-symmetry, which refers to the balanced and interactive distribution of technological capacity, information processing ability, and decision-making capability across production networks. This study proposes a comprehensive hardware–software–intelligence framework for a real-time visual quality inspection of transformer cases during the manufacturing process by using an embedded deep learning architecture. First, a real-world dataset consisting of 232 defective and 264 non-defective printed transformer case images was collected from the production line of a transformer manufacturing facility and preprocessed to improve data quality and model generalization. Second, to enhance feature extraction capability, the classical AlexNet architecture was modified to develop a Multiscale AlexNet (MS-AN) model capable of simultaneously capturing both global and local spatial features. The proposed architecture incorporates parallel convolutional branches with 3 × 3 and 5 × 5 receptive fields, which are fused at the feature level to increase representation diversity and improve robustness against noise and degradation in printed images. Third, an experimental system was implemented using practical industrial automation technologies (e.g., CUDA-accelerated C++ programming, the NVIDIA Jetson Orin Nano edge computing platform, ROS-based communication infrastructure, IoT protocols, and programmable logic controller (PLC) integration). Experimental results demonstrate that the proposed system achieves real-time inspection performance of approximately 2 s per inspection with 99% classification accuracy on the constructed dataset. The developed framework enables efficient deployment of deep learning models on GPU-based edge devices; thus, it reduces reliance on workstation-class computers, lowers energy consumption, and supports scalable intelligent inspection architectures aligned with Industry 4.0 transformation objectives.