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Live+1 todayUpdated 47m ago

JAC Debuts Industry's First CV Quality Inspection AI Model at Beijing Auto Show

JAC Group unveiled the automotive industry's first computer vision quality inspection large model at the 2026 Beijing International Automotive Exhibition, signaling its push into AI-driven manufacturing.

Parameter count of CV model

3 billion

Intelligent robots in factory

1,800

Maextro S800 units delivered

15,000

What Happened

JAC Group officially launched the MASTER CV Quality Inspection Large Model, described as the industry's first computer vision-based quality inspection AI. It uses a 3-billion-parameter mixture-of-experts architecture and can identify all categories of defects with millisecond-level edge inference. The system allows full-vehicle reuse after a single training session and provides 100% full-link traceability, aiming to greatly reduce defect outflow rates.

Alongside the AI model, JAC showcased several new vehicles including the DEFINE-X and DEFINE-S prototypes, the RF8 Enjoy Series MPV, and the T9 PHEV pickup. The company also signed a joint innovation agreement with Tsinghua University to deepen collaboration in cutting-edge technology. JAC emphasized its dual-driven strategy of independent development and open cooperation with partners like Huawei and CATL.

Why this matters

The model can detect defects in milliseconds and enable full-vehicle traceability, promising to dramatically reduce defect rates and set new quality benchmarks in car production.

Terms in This Story

Computer vision (CV) quality inspection large model
A large AI model that uses computer vision to automatically inspect vehicle quality and detect defects.
Mixture-of-experts architecture
A neural network design with multiple specialized sub-models that activate only for relevant inputs, improving efficiency and accuracy.
Plug-in hybrid electric vehicle (PHEV)
A vehicle that combines an internal combustion engine with a rechargeable battery, allowing limited electric-only range.
Read Original: JAC Motors

Summarised from the linked release; details can be imperfect — always verify against the original source.