Optimizing Real Time Defect Detection Algorithms in Industrial Assembly Lines Using Convolutional Neural Networks on ARM Cortex-M Microcontrollers

Author(s)	Akshat Bhutiani
Country	USA
Abstract	This paper proposes an optimal approach for real-time defect detection in industrial assembly lines using Convolutional Neural Networks (CNN’s) implemented on ARM Cortex – M microcontrollers. Although CNNs are very accurate when detecting visual defects, their computational complexity makes them difficult to implement on devices with limited resources. To address this issue, techniques such as model pruning, quantization and memory optimizations tailored to the ARM Cortex -M microcontroller will be used. This results in an average inference time of 80-100 ms per image and the system achieves 92% accuracy which makes it suitable for industrial applications.
Keywords	Real-Time Defect Detection, CNN, ARM Cortex – M, Model Pruning, Industrial Automation, Quantization
Field	Engineering
Published In	Volume 1, Issue 3, November-December 2019
Published On	2019-12-26
Cite This	Optimizing Real Time Defect Detection Algorithms in Industrial Assembly Lines Using Convolutional Neural Networks on ARM Cortex-M Microcontrollers - Akshat Bhutiani - IJFMR Volume 1, Issue 3, November-December 2019. DOI 10.36948/ijfmr.2019.v01i03.1723
DOI	https://doi.org/10.36948/ijfmr.2019.v01i03.1723
Short DOI	https://doi.org/g8d3pj

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research