Organisation
ACS Clothing Limited
Sector
Materials & Chemicals
Knowledge base
Heriot Watt University
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Challenge
ACS Clothing Limited, Europe’s largest circular fashion hub, faced challenges with manual garment inspections. Detecting defects such as stains and tears relied on human inspectors, which was time-consuming, prone to errors, and inconsistent. The company needed a solution that could improve accuracy, speed, and scalability while supporting its commitment to sustainable fashion. They partnered with Heriot-Watt University to explore AI and computer vision technologies for automated inspection, aiming to reduce labour costs and enhance quality control.
Outcome
The Innovate UK Accelerated Knowledge Transfer (AKT) project developed a comprehensive feasibility study evaluating multiple technologies for automated defect detection. The team tested approaches including computer vision machine learning, hyperspectral imaging, and visual large language models. Proof-of-concept systems were built, demonstrating the potential for real-time detection of stains and tears. The project also identified operational requirements, such as the importance of consistent lighting for accurate inspections. ACS now has a clear roadmap for integrating AI-driven inspection into daily operations and a framework to evaluate future technologies as they emerge.
Impact
The project has provided ACS with actionable insights and practical tools to implement AI in garment inspection. The company can now improve inspection efficiency, reduce human error, and save on labour costs. The iterative approach used during the project has de-risked future development, giving ACS confidence to scale automated inspection across different garment types. Beyond immediate operational gains, the collaboration strengthened ACS’s expertise in AI applications within the circular fashion industry, positioning the company as an innovator in sustainable garment processing. The findings also lay the groundwork for long-term integration of AI across other areas of production, enhancing competitiveness and supporting industry-wide adoption of smarter, more sustainable practices.
What they say
"The aKTP provided us with a structured, expert-led approach to better understand the complexities of automatically identifying defects in garments. The collaboration helped us explore viable technologies, reduce risk for future development, and move closer to integrating AI into our quality control processes."
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