Optimizing Machine Vision Systems
Vision System Performance
Optimization Beyond The Default Tools
For example, vertical and horizontal edge tools can sometimes replace pattern finders as fixturing tools, often at a fraction of the cycle time. Pattern finders themselves can also be optimized by limiting their rotation range, which can significantly improve performance.
Other techniques can improve reliability. Geometric constraints can help keep regions within the input image, preventing the common failure where a region falls outside the image boundary. Filtering tools can also be dynamically tuned to the brightness of the current image rather than relying on static settings that may not always be correct.
These optimizations require experimentation and creativity, and sometimes the right idea does not appear immediately. However, engineers should remember that the Cognex toolset is a deep collection of powerful capabilities. With thoughtful optimization, even challenging applications can often be transformed into reliable and efficient solutions.
Optimization Tips
Practical Ways to Improve Vision System Performance
Use Simpler Tools When Possible
Pattern finders are powerful, but they are not always necessary. If a task only requires locating a straight edge or simple feature, vertical or horizontal edge tools may provide faster and more stable results.
Limit Unnecessary Search Ranges
When pattern finding is required, restricting rotation ranges or search areas can significantly reduce processing time.
Control Regions With Geometric Logic
Regions that drift outside the image boundary are a common cause of system failure. Applying geometric constraints can help keep regions anchored within the usable image space.
Avoid Static Filtering Assumptions
Image conditions change. Filters that rely on fixed brightness or contrast settings may fail when lighting shifts. Instead, dynamically adjusting filtering parameters based on the current image can greatly improve robustness.
Think Beyond The First Working Solutions
A system that works is only the starting point. Revisiting the implementation and exploring alternative tools can reveal opportunities for significant improvements in both reliability and speed.
Leverage Process Knowledge
Traditional tools offer many approaches that capture the process history, its normal variability, the programmer’s experience, and the hierarchical stacking and/or branching of tools and tool groups for specific tasks. Deep learning, while powerful, ignores all that.