Real-time Analytics
Weiyi Manufacturing needed to reduce reliance on exhausting manual visual inspection in 3C factories because workers had to inspect thousands of parts from many angles every day, which caused fatigue, unstable quality, and persistent miss-detection issues with both human and traditional rule-based vision systems.
Reported impact
Weiyi improved inspection accuracy and speed, reduced quality-rework risk, and moved frontline workers from repetitive visual checking into more valuable analysis and labeling roles.
Weiyi Manufacturing and Baidu AI Cloud built an AI visual-inspection system for complex surface defects in 3C production. The solution combines Baidu's industrial quality-inspection capabilities with cloud infrastructure so line images can be captured, processed, classified, and shared digitally instead of relying on purely manual judgment. The inspection device can capture 18 to 24 images in about one second and uses a Baidu algorithm engine to process up to 24 models, classify more than 34 defect types, and complete the full decision pipeline in 480 milliseconds. Beyond detection, the system uploads inspection statistics such as daily output and defect information to the cloud for SPC-style analysis and digital sharing. The result is a full-angle visual-quality workflow that covers six product faces, identifies defect size, location, and shape automatically, and lets operators shift from repetitive visual screening to higher-value defect analysis and annotation work.
Production-line images are captured from multiple angles, processed by the algorithm engine to detect and classify defects, and then uploaded with output and defect statistics to cloud services for SPC-style analysis, reporting, and digital sharing back to factory operations.
After training on defects, the device can identify all 33 defect classes accurately, keep the miss rate below 0.1%, and deliver 6.5x ROI versus traditional vision machines (original: 能准确识别产品的全部 33 类缺陷，漏检率控制在 0.1% 以内…投资回报率是传统机型的 6.5 倍).
The Baidu algorithm engine can process 24 models simultaneously and complete the full workflow in 480 milliseconds (original: 百度算法机，能够同时处理 24 个模型，处理完所有流程仅需 480 毫秒).
The inspection device can extract 18 to 24 images in one second and mark more than 34 types of defects in real time (original: 能在 1 秒之内提取 18 到 24 张图片…超过 34 种缺陷被实时标记了出来).
Weiyi Manufacturing and Baidu AI Cloud built an intelligent quality-inspection solution that automates 3C visual inspection with AI vision technology (original: 微亿智造联合百度智能云打造智能质检解决方案，通过AI视觉检测技术实现3C制造质检自动化).
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Baidu AI Cloud Sample Dec 26, 2025 China Iron and Steel Research Institute Group (中国钢研科技集团) China Iron and Steel Research needed a practical way to apply large-model and visual AI capabilities across metallurgical research and manufacturing, especially for metallography analysis, surface defect detection, and… 100 ms response time Baige AI Heterogeneous Computing Platform · Kunlun · Qianfan Large Model Platform 
NVIDIA Sample Aug 25, 2026 Siemens Siemens needed to help industrial teams overcome fragmented engineering tools, labor shortages, and slow design and maintenance workflows so they could design, simulate, and operate complex products and factories faster… 10%–15% reductions in capital expenditure NVIDIA CUDA · NVIDIA DGX B200 · NVIDIA DGX H200 
In-house Sample Aug 13, 2026 Lockheed Martin Static flight-hour tables and usage metrics ignored real-time consumption, causing chronic under-resourcing, limited stock effectiveness, and a reactive sustainment supply chain that could not keep pace with modern… predict component failure up to 72 hours before it occurs LogC2 dashboard · deep-learning models · digital data fabric 