Computer Vision for Quality Control Challenge

African manufacturers and artisans lose billions in rejected exports and local sales due to inconsistent quality control, lacking the expensive inspection equipment and trained personnel that international competitors possess. Small textile producers cannot detect weaving defects that cause entire shipments to be rejected, furniture makers miss structural flaws that damage brand reputation, food processors cannot ensure consistent standards for export certification, and electronics assemblers lack tools to verify component placement. Developers must create smartphone-based computer vision systems that detect defects in real-time across textiles, furniture, food, and electronics, working in challenging lighting conditions with minimal training data, enabling African producers to compete globally through consistent quality assurance.

The Computer Vision for Quality Control Challenge seeks innovative AI-powered visual inspection systems that democratize quality control for African manufacturers, artisans, and food processors. This challenge addresses the critical need for affordable, accessible quality assurance technology that can help local producers compete in global markets while maintaining consistent standards.

Participants will develop on-device computer vision systems that use smartphone cameras to detect defects and ensure quality in textiles, furniture, food processing, electronics assembly, and traditional crafts. The system must work in real-time, provide immediate feedback to workers, and operate entirely on-device without requiring internet connectivity or expensive specialized hardware.

Successful solutions will implement efficient deep learning models optimized for mobile devices, handle varying lighting conditions common in informal manufacturing settings, detect subtle defects that affect product quality, and provide clear visual feedback to guide corrections. The system should learn from local examples, adapt to different product types, and maintain accuracy across diverse manufacturing environments.

We particularly value solutions that can be trained by non-technical users with minimal examples, provide quality metrics and reporting for certification purposes, integrate with existing production workflows, and support multiple African industries. The platform should help small manufacturers meet export standards, reduce waste, and improve competitiveness in global markets.