A comparative evaluation demonstrates effective defect recognition in aerospace quality control systems, indicating robust solutions for operators.
The increasing adoption of Artificial Intelligence (AI) is reshaping quality control in critical sectors. Within the AVIS project, this study presents a benchmark of AI-based automated visual inspection systems. Ten commercial solutions were analysed through a systematic requirement assessment, and the system was validated using a dedicated dataset. The aim was to identify the solution that best meets requirements while supporting operators during inspection. A case study with Avio Aero enabled the design and deployment of the system in a real production environment. Focused on detecting defects in aeronautical power transmission components, the system ensures high quality standards and contributes to Zero Defect certification. Its On-Premises/Edge architecture supports local processing, reducing latency and improving security. The implementation followed five phases, from data collection and model training to real-time testing. Results confirm the effectiveness of the system in defect recognition and validate the methodology as decision-making tool for industrial technology adoption.
No takes yet. Share an insight, caveat, or question.
Corallo et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: