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AI Visual Inspection
AI-based quality inspections—such as appearance checks, dimension measurements, and missing-part detection—boost efficiency by 80%, reduce false-positive rates by 80%, and support intelligent decision-making on the production line.
Get the planThe BangQi AI Visual Inspection Solution leverages BangQi Technology's self-developed industrial AI small models and image recognition algorithms to deliver an integrated, intelligent quality inspection system tailored for smart manufacturing production lines. The system combines high-resolution industrial cameras, advanced deep learning algorithms, and an edge computing platform, making it suitable for a wide range of quality control applications—including appearance defect detection, precise dimension measurement, and identification of missing parts.
This solution boasts significant advantages—high precision, fast pace, full automation, and strong adaptability—making it an effective replacement for traditional manual quality inspections. It helps reduce labor costs, minimizes human error, and is ideally suited for industries with stringent demands on product consistency and yield, such as new energy batteries, 3C electronics, automotive parts, and pharmaceutical packaging.
Key Highlights
Quality inspection efficiency improved by up to 80%
Compared to manual inspection, AI visual systems can enable more frequent, higher-paced, and continuous quality checks, ensuring uninterrupted production.
False positive and false negative rates reduced by 80%
By continuously optimizing the recognition model through deep learning algorithms, we have significantly reduced missed detections and false alarms in traditional quality inspections.
Reduce manual labor by more than 50%
The system operates reliably 24/7, completely replacing manual visual inspections and enabling reduced workforce deployment—and easing labor-related pressures—on the production floor.
Simultaneous identification of multiple defect types
Supports the identification of over ten types of visual and structural defects, including scratches, dents, burrs, stains, material drop-offs, misassemblies, and missing parts.
Continuous self-learning and self-optimization
Powered by a self-developed AI small-model data foundation, the model continuously evolves and improves its accuracy as more detection samples are accumulated.
Technological Advantages
Pongqi Industrial AI Small Model Foundation
Built on industrial knowledge graphs and process data, it can maintain high recognition accuracy even in scenarios with low image quality and complex environments.
One-stop testing architecture
Integrating image acquisition, processing, analysis, decision-making, and feedback, the system can be seamlessly embedded into production lines for quality inspection.
High-Precision Image Acquisition System
Supports configurations such as 20-megapixel industrial cameras, multispectral imaging, and 3D scanning, catering to various workpiece sizes and materials.
Edge Computing + Cloud Collaboration
The detection algorithm runs locally in real time, ensuring rapid response; meanwhile, unified model training and upgrades are conducted in the cloud, enabling fast iteration and continuous optimization.
Highly compatible and highly scalable
Modular hardware and software architecture that can seamlessly integrate with systems like MOM, MES, and PLC, enabling production line coordination and automated traceability.
Industry Applications
New Energy Manufacturing:
Cell/Module Appearance Defect Detection
High-speed detection of critical quality issues such as black spots, scratches, bulges, and improper edge sealing, ensuring the safety of battery products.
3C Electronics:
Precision Component Dimensional/Missing Part Quality Inspection
Performing millimeter-level dimensional measurements and assembly verification for micro-components such as chips, terminals, and connectors.
Automotive parts:
Complex Surface Defect Detection
Achieve high-precision inspection of complex surfaces such as lamps, interiors, and metal components, reducing the rework rate.
Pharmaceutical packaging:
Bottle cap, label, and packaging integrity inspection
Meet GMP regulatory requirements, enable automated drug packaging inspection at high speeds, and enhance yield rates and traceability.
Customer Case
A Large Pharmaceutical Company's AI-Based Inspection Project for Automated Packaging Lines
Scenario issue: Improperly sealed bottle caps, crooked labels, and damaged box packaging—all of which affect compliance rates.
Deployment plan: Custom multi-view image capture system combined with structured-light positioning, integrated into the existing packaging station.
Result achieved: The defect rate dropped from 3.2% to 0.5%, and daily quality inspection capacity exceeded 80,000 units—eliminating the need for manual secondary verification.
An AI-based quality inspection system for the module production line at a leading lithium-ion battery company
Scenario issue: Traditional manual quality inspection can't keep up with the production line's pace, leading to frequent omissions and misjudgments.
Deployment Plan: Install 5 AI-powered visual inspection stations to cover the appearance of both module sides, as well as the ear-terminating encapsulation areas.
Resulting impact: Detection efficiency improved by 80%, quality inspection manpower reduced by 70%, and the system’s self-learning accuracy has increased to over 98%.
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