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.
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
Detecting defects related to missing components or misassembled assemblies during inspection.
Examples: Detection of missing parts, misalignment of seals, and other part defects
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
Counting number of parts
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
|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.|