AI-Powered Tyre Inspection System Modernizes Quality Control with Speed and Precision
Traditional tyre inspection methods, dependent on manual checks, are often slow, inconsistent, and vulnerable to errors — impacting both quality and production efficiency. This smart quality inspection system transforms tyre evaluation by harnessing the power of AI, significantly reducing inspection time while elevating accuracy and consistency. Built on an advanced AIoT (Artificial Intelligence of Things) architecture, the system integrates computer vision, mechanical automation, and patented machine learning algorithms to detect and classify over 60 types of defects. From tread irregularities and sidewall imperfections to structural anomalies, every critical aspect is covered — ensuring faster, more reliable inspections and helping manufacturers maintain superior quality standards at scale.
Machine Learning algorithm Computer Vision
CNNs, R-CNN, Image classification
Object Detection and Anomaly Detection Techniques
Proven capability in developing machine learning models for defect classification using CNNs, object detection, and anomaly detection techniques tailored for tyre inspection.
Expertise in building 3D profile and 2D image extraction pipelines to analyze tyre treads, sidewalls, and structural components with high accuracy.
Delivering solutions based on AIoT architecture that combines vision systems, mechanical automation, and edge computing for real-time inspection and classification.
Designing real-time processing models capable of full tyre inspection under 30 seconds, optimized for edge devices with GPU acceleration.
Integrating AI inspection modules seamlessly with tyre production lines, PLCs, and MES systems to automate quality checks and minimize manual intervention.
Experience in developing patent-pending or proprietary AI algorithms that offer clients a competitive edge in defect detection and quality assurance.
Engineering flexible systems supporting inspection across varying tyre dimensions (from 12” to 18” and beyond), adaptable to diverse manufacturing setups.
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