AI-Powered Visual Defect Detection
Smart Manufacturing using AI
DefectGuard by Brightpoint AI is a visual inspection solution powered by AI, enhancing quality and efficiency within the manufacturing sector. Leveraging adaptive AI, it actively monitors and adjusts to shifts in the manufacturing environment. Additionally, it incorporates unsupervised learning to classify defect types and uncover novel ones.
With a swift implementation timeline, the solution seamlessly integrates within days. It provides flexible deployment choices, allowing real-time implementation either on-premises or in the cloud. This empowers high-volume production lines to leverage AI for the prompt identification of sub-quality products.
DefectGuard Automates Defect Detection, Boosting Efficiency And Accuracy
Key Benefits of using DefectGuard
Real time defect detection
Always on (24/7) fully automated defect detection
High accuracy, able to detect defects invisible to naked eye
Improve speed of defect detection and corresponding removal of defective item
Multiple items can be inspected at same time with same defect detection process
Less dependency on human action
Improve plant and worker safety
Enhance worker productivity
Free up time of quality manager or machine operator for more productive tasks by using their expertise
Can easily spot new and varied defects without change in hardware or iot sensors
DefectGuard offer several Advantages over Traditional QA Systems
Tailored for Manufacturing Professionals: Empowering them to harness AI's capabilities without the need for Data Science or programming skills.
Instant, Large-scale Detection: Identifying flaws and irregularities within seconds, facilitating the integration of AI into high-volume production lines for efficient removal of sub-par products.
Adaptive, Self-repairing AI: Continuously monitoring and adjusting to environmental changes that induce data drift, such as variations in lighting, camera misalignment, and production rate fluctuations.
Compatibility with Any Camera, Still or Video: Capable of working seamlessly with virtually any camera providing consistent images at the required resolution, whether capturing video or still shots.
Swift Implementation: Easy deployment, thanks to its user-friendly interface, data connectors, and built-in automation.
Deep Learning Vision AI Defect Detection Model
To automate hard-to-see defect detection, manufacturers streamline the visual inspection process with vision AI to achieve a higher production output.
We use Deep Learning AI to create our Defect Detection Model. Deep learning (DL) has proven to be a perfect tool for image processing and pattern recognition, as automatic feature-learning abilities of deep methods have made them superior. This means the model self learns and does not need to be trained repeatedly.
Real-time Analytics and RCA
Dashboard that offers real-time insights, customizable reports, and historical data trends, along with pattern analytics
Advanced Data Analytics empower plants to
Determine optimal operating conditions for maximizing production and ROI.
Identify the root cause of defects to prevent manufacturing defects in the future.
Pinpoint areas in the value chain that require changes to enhance the efficiency of the production process.
Identify supply chain issues and provide insights into suppliers.
Cloud or On Premise Edge Computing Capabilities
Secure System Ensuring Data Privacy and Protection
Numerous sectors benefit from the application of Visual Inspection and Image Processing using AI in defect identification. Here are several instances:
DefectGuard can automatically inspect the fabric patterns and detect any defects such as holes, stains, or irregularities. The system can use a deep learning algorithm trained on images of good and bad fabric samples, and flag any anomalies in real time. This can improve the quality control process and reduce the waste of resources and time.
A food processing company can use a DefectGuards to monitor the quality and safety of the food products, such as fruits, vegetables, meat, or dairy. The system can use a deep learning algorithm trained on images of different food categories and their quality standards, and identify any defects such as bruises, mold, rot, or contamination. This can ensure the compliance with food regulations and prevent foodborne illnesses.
DefectGuard can inspect the shape, size, and appearance of the glass or bottle products, and detect any defects such as cracks, chips, bubbles, or scratches. The system can use a deep learning algorithm trained on images of various glass or bottle types and their specifications, and measure any deviations from the desired quality. This can improve the customer satisfaction and reduce the risk of product recalls.
3 Phase Deployment Process
DefectGuard seamlessly integrates with a diverse array of cameras, spanning both still and video formats. Existing cameras can leverage pre-existing images, while the installation of new cameras or automation equipment is supported through collaboration with DefectGuard's ecosystem partners, ensuring a comprehensive solution.
Users gain access to camera-generated images, which they then upload into DefectGuard. The choice between a supervised approach—where users label images as "good" or "bad"—and an unsupervised approach, where DefectGuard autonomously clusters images, empowers users to address known and unknown issues.
Training: Initiating the AI model training process with a simple click typically results in a fully trained model in under 30 minutes. The model's predictions and accuracy are promptly displayed. If the results fall short, additional image uploads fine-tune the model until the desired accuracy is achieved.
Deployment involves streaming real-time images to DefectGuard, which in turn provides a continuous flow of predictions, categorizing images as "good" or "bad." This prediction stream can be seamlessly integrated into a sorting mechanism or reviewed by an operator for further analysis and decision-making.
Improve Accuracy | Increase Efficiency | Minimize Downtime
Enhanced Precision: DefectGuard improves precision by overcoming the limitations of the human eye in making precise measurements, especially on a tiny scale.
Objective Measurements: Unlike the human eye, which may overlook subtle differences, DefectGuard ensures objective measurements, including surface roughness, size, and other critical factors.
High Optical Resolution: Machine Vision, a component of DefectGuard, boasts high optical resolution determined by advanced technology and equipment for image acquisition. This surpasses the visual capabilities of the human eye.
Expanded Visual Spectrum: Machine Vision extends visual perception beyond human capabilities, enabling observations in Ultraviolet, X-ray, and Infrared regions of the spectrum.
Swift Observations and Conclusions: DefectGuard processes observations and draws conclusions rapidly, leveraging the computational speed. This leads to fast and precise calculations.
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