Data Collection: Gathering a diverse set of hose images under various lighting and positioning conditions.
Annotation: Labeling the collected images to identify defective and non-defective areas.
Model Training: Using annotated data to train a machine learning model to detect defects.
Inference: Deploying the model to analyze new images in real time.
Real-Time Quality Check: Enabling on-the-fly inspection at production lines for immediate quality
feedback.
Our AI-powered quality control system uses cameras to capture hose images from multiple angles with dynamic lighting adjustments to enhance defect visibility. These images are then processed on an AI server where:
The trained model analyzes the images to assess quality.
If the hose meets all standards, the system passes it.
If any defect is detected, the system flags it as a fail.
This automated process significantly reduces human dependency, speeds up inspection, and enhances overall product reliability.
80%+ reduction in manual inspection time
Consistent defect detection with improved accuracy
Scalable and real-time deployment on production floors