Detection of Component Assembly Error using Computer Vision: A Review
Rahul S. Jain1, Nikhil P. Wyawahare2, Arpit Doshi3
1Rahul S. Jain, Research Scholar PG VLSI, Department of Electronic Engineering, G H Raisoni College of Engineering, Nagpur, India.
2Nikhil P. Wyawahare, IEEE, Assistant Professor, Department of Electronic Engineering, G H Raisoni College of Engineering, Nagpur, India.
3Arpit Doshi, Manager COE Paint Shop & Machine Shop, Mahindra & Mahindra Ltd, Nagpur, India.
Manuscript received on July 16, 2020. | Revised Manuscript received on July 29, 2020. | Manuscript published on August 10, 2020. | PP: 402-405 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.I7042079920 | DOI: 10.35940/ijitee.I7042.0891020
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object recognition (OR) is a main capability needed by most AI vision systems. The most recent R&D on this domain has been gaining incredible ground in numerous ways. OR has a variety of uses. In this paper we talk about applications of OR system in manufacturing industry. In recent era scenario increased level of process automation in production industry also demands process automation of quality examination with lesser human intervention.
Keywords: Detection System, Object Extraction, Object Recognition, Object counting, Deep Learning, Computer vision, AI, Machine Learning.
Scope of the Article: Machine Learning