This paper presents a lightweight and cost-effective computer vision solution for automated industrial inspection using You Only Look Once (YOLO) v8 models deployed on embedded systems. The YOLOv8 Nano model, trained for 200 epochs, achieved a precision of 0.932, an mAP@0.5 of 0.938, and an F1-score of 0.914, with an average inference time of ~470 ms on a Raspberry Pi 500, confirming its feasibility for real-time edge applications. The proposed system aims to replace physical jigs used for the dimensional verification of extruded polyamide tubes in the automotive sector. The YOLOv8 Nano and YOLOv8 Small models were trained on a Graphics Processing Unit (GPU) workstation and subsequently tested on a Central Processing Unit (CPU)-only Raspberry Pi 500 to evaluate their performance in constrained environments. The experimental results show that the Small model achieved higher accuracy (a precision of 0.951 and an mAP@0.5 of 0.941) but required a significantly longer inference time (~1315 ms), while the Nano model achieved faster execution (~470 ms) with stable metrics (precision of 0.932 and mAP@0.5 of 0.938), therefore making it more suitable for real-time applications. The system was validated using authentic images in an industrial setting, confirming its feasibility for edge artificial intelligence (AI) scenarios. These findings reinforce the feasibility of embedded AI in smart manufacturing, demonstrating that compact models can deliver reliable performance without requiring high-end computing infrastructure.

The growing convergence between Information Technology (IT) and Operational Technology (OT) has reshaped industrial environments by enabling digital integration, advanced analytics, and cyber-physical connectivity. However, this integration also increases the exposure of industrial systems to cyber risks, interoperability challenges, and governance complexity. This study proposes and applies a six-dimensional maturity assessment framework to evaluate the current state of IT/OT convergence in Brazilian industrial organizations. A structured survey using Likert-scale metrics was administered to professionals across multiple industrial sectors, capturing perceptions of system integration, interoperability, cybersecurity practices, governance structures, data management, and enabling technologies. Results reveal intermediate maturity levels in system integration and interoperability, while cybersecurity, data governance, and coordinated IT/OT management remain underdeveloped. The findings highlight persistent barriers to digital transformation and reinforce the need for targeted governance, cybersecurity strategies, and data-driven practices in industrial operations. The study contributes a preliminary diagnostic tool that can be replicated and expanded in future evaluations of digital maturity within Industry 4.0 environments.