Design of image classification system for fabric inspection process using Raspberry Pi - Nugroho, Emmanuel Agung; Setiawan, Joga Dharma; Munadi, Munadi; Diki, Diki (Journal of Mechatronics, Electrical Power, and Vehicular Technology, 2024)
This research is designed as a prototype of defect inspection system on fabric production using machine learning-based image processing technology using the open source Google teachable machine application integrated with Raspberry Pi-3B. The prototype of fabric defect inspection system is built by utilizing two rollers that function as a fabric roll house before and after the inspection process. On both rollers, a fabric is stretched to be inspected, so that from a roll of fabric with a certain length, it can be seen how many defects occur on the fabric. The inspection system is carried out using a web camera with a certain level of lighting connected to a raspberry pi as a control device. Raspberry Pi functions as an image processing device and fabric rolling motor controller. In addition to the category of fabric in good condition, this system classifies into two categories of defects, namely slap defects and sparse defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an accuracy of image classification results of 98.4 %.