Advancements in deep learning based visual and robotics applications and systems for quality control in Industry 5.0

The emergence of Industry 5.0 marks a paradigm shift toward human-centric, sustainable, and resilient manufacturing. Unlike its predecessor, Industry 5.0 emphasizes close collaboration between humans and intelligent systems, integrating advanced robotics, artificial intelligence (AI), and deep learning technologies to create more adaptive, flexible, and personalized production environments. In this context, visual and robotic systems for quality control play a pivotal role in achieving higher efficiency, precision, and adaptability in manufacturing processes.

This Research Topic aims to gather cutting-edge contributions focused on the development and application of deep learning-based visual inspection and robotic systems designed for quality control in Industry 5.0 settings. The integration of advanced imaging techniques with AI-driven analysis enables real-time detection of defects, predictive maintenance, and continuous process optimization. Moreover, the synergy between collaborative robots (cobots) and intelligent vision systems enhances human-machine interaction, enabling safer and more efficient quality assurance workflows.

Potential areas of interest include, but are not limited to:

- Novel deep learning architectures for industrial image analysis
- Vision-based robotic manipulation and assembly for quality inspection
- AI-powered multi-modal sensor fusion for defect detection and monitoring
- Real-time quality control systems integrating computer vision and robotic automation
- Human-centered systems that combine operator expertise with AI for enhanced decision-making
- 3D imaging and multi-view inspection systems
- Hybrid human-robot collaboration in inspection tasks
- Self-supervised and unsupervised learning for anomaly detection
- Edge AI and on-device learning for real-time inspection
- Digital twins for quality prediction and proactive maintenance
- Sustainability-oriented visual quality control
-Generative AI and simulation for synthetic data creation

Additionally, this Research Topic encourages studies exploring the role of explainable AI (XAI) in making deep learning models more transparent, interpretable, and trustworthy for quality control applications. Contributions addressing sustainability and resilience through intelligent inspection systems, as well as methods that promote human-centric design and ergonomics in Industry 5.0, are highly welcomed.

Through this Research Topic, interdisciplinary discussions and innovative approaches that bridge the gap between AI, robotics, and advanced imaging in modern manufacturing are encouraged.

Researchers and practitioners from academia and industry are invited to share original research articles, reviews, and case studies that contribute to advancing the future of intelligent quality control systems—ultimately paving the way toward more adaptive, efficient, and human-centered industrial ecosystems.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Deep Learning-based Visual Inspection, Collaborative Robotics, cobotics, Human-Centric Quality Control, Industry 5.0 Manufacturing, AI-driven Defect Detection

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.