AI Powered Inspection – Enabling Superior Results, even with limited training data.
Based on our proprietary AI technology, SPECTRO delivers faster and more accurate results, even with small amounts of training data.
Where traditional AVI / AOI systems are prone to false positives, SPECTRO excels and enables factory automation by vastly reducing the amount of reclassification needed by human inspectors.
SPECTRO Product Line-up
Use Case Examples
Defect Detection
Detecting defects in a wide range of materials, including wood, metal, and fiber, using only normal product specifications.
Examples: Detection of flaws, dents, defects, and breakage
Missing parts & Misalignment Detection
Detecting defects related to missing components or misassembled assemblies during inspection.
Examples: Detection of missing parts, misalignment of seals, and other part defects
Pattern Matching
Detecting defects related to deviations from the specified object pattern in addition to inspecting object uniformity.
Examples: Soldering inspection & identification of circuit board breakage points
Examples of Inspection Subjects
Printing condition
Printer cartridges
Metal plating
Counting number of parts
PCB
Solar panels
Wood parts
Inspection Performance
Visual Inspection for Solar Cells
Listed below is a comparison between our Visual Inspection AI, a classifier (SVM), and a Deep Learning method (CNN) for defect detection in solar cells. Compared to the other methods, our AI performed with higher accuracy and speed, even when the training data was limited.
* Sergiu Deitsch et al. : Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images. 2019| SVM | CNN | SPECTRO | |
|---|---|---|---|
| training data size | 800 models | 800 models | 60 models |
| Training time | 30 min. | 5 hr. | 19 sec. |
| Inference time | 8 min | 20 sec. | 10 sec. |
| Accuracy | 85% | 86% | 90% |