AI Fabric Inspection and Low-Carbon Dyeing Breakthroughs: Keqiao Conference Reveals New Inflection Point for Textile Digitalization

AI fabric inspection is moving from concept validation to production line deployment, but the lack of a unified industry defect database remains the biggest bottleneck for large-scale adoption. This consensus emerged among technical experts and printing and dyeing enterprise representatives at the 2026 Textile Industry Digitalization Development Conference held in Keqiao on May 7.

Data and Trends: Hard Targets for Water Saving, Carbon Reduction, and Efficiency

Yan Yan, Vice President of the China National Textile and Apparel Council, stated in her speech that the application of AI in the textile industry is rapidly moving from concept validation to large-scale implementation. Looking ahead to the 15th Five-Year Plan, three major directions were clarified: promoting deep AI deployment to build a 'textile intelligent large model' and vertical small models; deepening full-chain efficient collaboration to break down data silos; and deeply integrating green manufacturing to build a carbon footprint tracking model covering the entire industry chain.

The TDSD (Textile Digital Smart Dyeing) low-carbon digital dyeing process released by Hangzhou Huanyu Digital Technology provides quantifiable benchmarks: nearly 99% water saving, 33% carbon reduction, and 21% chemical reduction. This data indicates that the high water consumption and high emissions of traditional dyeing processes are being cracked by fully self-developed inkjet equipment and AI color management systems. The 'print-dye instantly' production model not only lowers the threshold for environmental compliance but also significantly enhances flexible supply capabilities.

Industry Impact: Dual Implementation of AI Inspection and Smart Dyeing

Liu Zhen, General Manager of Shanghai KaiQuan Intelligent Technology, pointed out that the core of AI fabric inspection implementation lies in building a 'teachable' self-learning system. Through software-hardware co-design, both detection efficiency and defect recognition accuracy achieve dual improvement. However, Luo Yucheng, Deputy General Manager of Shaoxing Keqiao Weaving, Printing and Dyeing Industry Brain Operation Company, admitted that the industry still faces challenges such as algorithm adaptation difficulties for complex fabrics, system instability in complex environments, and high implementation costs for SMEs. He suggested breaking through from three aspects: technological breakthroughs, cost optimization, and flexible transformation.

Long Fangsheng, General Manager of Zhejiang Meixinda Textile Printing and Dyeing Technology, shared a digital evolution path of 'shaping, soul-casting, and intelligence-enabling', emphasizing that smart factories are not cost centers but order creation centers. Printing and dyeing enterprises should break away from the simple positioning of fabric production and use digital upgrades to create incremental value.

Practical Recommendations

For Buyers - Prioritize suppliers with 'teachable' self-learning AI inspection systems, which can continuously adapt to new fabric defect types, reducing long-term quality control risks. - When evaluating dyeing processes, require suppliers to provide water-saving and carbon-reduction data for low-carbon processes like TDSD, and incorporate this into procurement scoring to meet future green compliance requirements.

For Foreign Trade Enterprises - Proactively build AI inspection capabilities and establish internal defect databases aligned with industry standards to shorten overseas customer factory audit cycles and increase order conversion rates. - Pay attention to the 'live streaming + platform + cross-border e-commerce + overseas warehouse' model for the full digital trade chain, leveraging digital platforms in industrial clusters like Keqiao to achieve real-time collaboration in cross-border supply chains.