Open AccessMicro Components Detection Using Deep Learning
Author(s) -
Satwant Kumar,
Hasan Phudinawala,
Abhishek Dhakan
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-2991
Subject(s) - automation , computer science , task (project management) , detector , electronics , product (mathematics) , deep learning , position (finance) , point (geometry) , production (economics) , scale (ratio) , artificial intelligence , engineering , electrical engineering , systems engineering , telecommunications , business , mechanical engineering , physics , geometry , mathematics , finance , quantum mechanics , economics , macroeconomics
Industries have to manufacture products in large scale with respect to time in order to compete others but while producing small electronic components goods in bulk where it is automated at some point. Sometimes due to malfunction in automation it produces defects in product which impact negatively in some percentages of production. So, we propose them with our project which will identify the product before getting out of manufacturing unit surveillance camera’s with highly trained model to specific task in detection can opt out defects from it. After placing them in required position they detect the products with defects or for particular detection which will make manufacturer hectic & complex work easy especially detecting micro components in it. In can be also cost reduction as we use raspberry pi. Infact all small industrial markets were facing issues to defects detection in small parts of electronic item like semiconductor with mobility devices. Are article is proposing a defect detection algorithm for micro components that are based on a single short detector network (SSD) and deep learning.