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Deep learning for advanced pose estimation and quality inspection in Industry4.0 assembly applications
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DOI：10.1080/0951192X.2026.2664186.png)
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Abstract
En 中文
The advances in robotics, coupled with the growing integration of cutting-edge technologies such as Artificial Intelligence and, specifically, Deep Learning within manufacturing, have enabled the development of flexible robotic systems capable of understanding their environment and executing tasks autonomously. This paper introduces a unified vision-based framework for industrial assembly applications that integrates Deep Learning – based object pose estimation and automated quality inspection within a single system architecture. The framework employs two complementary perception pipelines, combining semantic segmentation and direct 6D pose estimation models to accurately handle objects with diverse geometries and precision requirements. In parallel, a Deep Learning – based object detection module enables real-time quality inspection by identifying missing, misaligned, or incorrectly placed components. The overall framework is designed from a deployment-oriented perspective, supporting seamless integration of perception outputs within robotic control and monitoring systems while satisfying real-time industrial constraints. The framework is applied and validated on an industrially inspired case study from the consumer electronics sector, focusing on the assembly and inspection of a trimmer head and a monitor cover. The obtained results demonstrate accurate pose estimation, effective defect detection, and system-level integration in a realistic production context, highlighting the applicability of the proposed approach to flexible manufacturing environments.
Keywords:
Deep learning
artificial intelligence
machine vision
pose estimation
quality inspection
Industry4.0
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2.3K
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3.4K
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