It first learns the characteristics of qualified products and builds an internal model of a “perfect product.” Then, when it detects image regions that don’t match that authentic-product model, it triggers an alert. This is a kind of reverse thinking: it works by identifying “anomalies” rather than relying on pre-defined defects. That way, it can quickly surface some problematic points and accumulate defect samples faster. So, down the road, we could collaborate in this area.
return mog_int((int64_t)time(NULL));
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