AI Visual Inspection Implemented

Recently, many large busduct enterprises have deployed AI visual inspection systems based on deep learning in their smart factories, marking a substantial step forward for the busduct manufacturing industry in intelligent manufacturing and quality control. Equipped with an array of 20-megapixel high-resolution industrial cameras, the system captures high-definition real-time images of busduct conductor surfaces, insulation layer wrapping, and joint assembly at a speed of 60 frames per second. The collected image data is processed by dedicated image processing servers, where trained algorithm models complete defect judgment within 100 milliseconds.

According to the technical director of the project, the system adopts an advanced deep learning architecture. By learning from over 100,000 annotated samples, it can accurately identify 27 types of common defects, including micro-scratches, indentations, insulation damage, and uneven plating. Preliminary operation data shows that the system reduces the missed detection rate-commonly around 5% in traditional manual visual inspection-to below 0.1%, significantly lowering the ex-factory defect rate by 85% while increasing inspection efficiency by more than 300%.

The implementation of this innovative inspection solution not only significantly improves the consistency and reliability of product quality and reduces after-sales maintenance costs but also sets a new technical benchmark for industry quality control. Enterprises using this system can promote it to other production bases or export relevant technical solutions to industry partners, driving the intelligent transformation and upgrading of the entire busduct manufacturing industry.