AI Sensor for Defective Product Inspection/Detection through Image Recognition

We have started offering the unsupervised anomaly detection of the AI visual inspection platform "DeepAge Sensor" as an inference library (DLL for Windows) that can be integrated into inspection devices. The model, which is trained only on good product images, can be executed on the device without the need for defective product images in the training process. It operates solely on the CPU without requiring GPU or CUDA, and it completes all processes internally without external communication. It can be integrated using a C language interface from C/C++/C#, and it can be shipped with our company's code signing (Authenticode).

The AI visual inspection platform "DeepAge Sensor" has added a multi-stage fine-tuning feature. Models created through fine-tuning can be designated as the base model for subsequent additional training. By simply uploading additional images collected on-site, the model can be updated without retraining from scratch, and it can be stacked up to five times within the same lineage. The request list allows you to check the generation and lineage (source of derivation). Additionally, the verification tool "Sensor AD" has been enhanced with a score graph to select the decision threshold.

We have released three technical documents for the AI visual inspection platform "DeepAge Sensor" for free. The document titled "How to Launch Visual Inspection AI in a Process with No Defective Samples" (12 pages) summarizes five steps to establish anomaly detection using only good product images, along with pitfalls identified through real data validation. Additionally, we have prepared a product catalog (14 pages) and a "Guide to Integrating AI into Inspection Equipment" (13 pages) for equipment manufacturers. You can download them by providing your name, company name, and email address.

We have started offering a desktop application called "Sensor AD" that learns the "normal standard" only from good product images and detects images that deviate from this standard as anomalies. There is no need to prepare a large number of defective product samples, allowing for the implementation of anomaly detection operations from the initial stages even in environments where collecting defective products is difficult. The inference runs on the CPU, and a GPU is not required. It features visualization through contribution heatmaps and automatic clustering of false positives.