# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 5 Discussion  
This study offers valuable insights into the integration of traditional and AI-based approaches in industrial vision systems, particularly in the context of optimizing inspection processes in automated environments. By comparing manual learning and AI-driven methods, we were able to highlight the strengths and limitations of each approach, shedding light on how they can be effectively combined for enhanced performance.  
The use of manual learning in solving design tasks presents several advantages, particularly in the analysis of measured parameter values. For instance, the ability to convert pixel distances into SI units using special calibration tables is a significant benefit. Moreover, this method allows for the intentional omission of defects in areas deemed unimportant from a quality perspective. This targeted approach not only enhances efficiency but also ensures that critical areas are given priority. The simplicity of configuring functions in this mode, coupled with the low hardware requirements, further supports its practicality. Notably, the time required for image processing--from capture to analysis--was relatively short, ranging from 30 to 200 ms, depending on the detail's position relative to the reference image. This efficiency, combined with the fact that standard In-Sight system tools are freely available and highly customizable, makes manual learning a viable option in certain contexts.  
However, the study also revealed that the manual approach has limitations, particularly its restricted ability to control only specific, pre-defined values. This means that defects in unexpected areas may go undetected, potentially leading to false positives where defective details are mistakenly classified as good.  
In contrast, the AI-based approach, utilizing the ViDi tool, offers a more universal solution for comprehensive image analysis. This method does not require the teaching of specific features but instead relies on a set of reference images, from which the system autonomously identifies patterns associated with a good detail. Such an approach is highly effective in capturing any deviations from the established standard. This universality marks a significant advancement in inspection technology, offering the potential to detect a broader range of defects.  
However, our findings also indicate some drawbacks of the AI-based method. Access to the ViDi tool requires a key, adding an extra step to the process. Additionally, the need for high computational power can be a barrier, as lower-powered systems may experience significantly longer processing times. Moreover, the AI-based approach provides a general similarity score rather than identifying specific defects, which could limit its utility in certain scenarios. The image processing time for this method was longer, ranging from 227 ms to 262 ms, which could impact overall efficiency in time-sensitive environments.  
In conclusion, our study suggests that a hybrid approach, integrating both manual and AI-based methods, could offer the best of both worlds. By leveraging the precision and customization of manual learning alongside the broad, pattern-recognition capabilities of AI, future developments in industrial vision systems could see significant improvements in both accuracy and efficiency. This integrated approach holds promise for optimizing inspection processes, ensuring that automated environments are both more reliable and adaptable to diverse inspection needs.