Paper Info
Title
A VLSI image processing architecture dedicated to real-time quality control analysis in an industrial plant
Abstract
In this paper, we present a VLSI architecture for real-time image processing in quality control industrial applications: automation of the visual inspection phase of mechanical parts treated by the Fluorescent Magnetic Particle Inspection method for structural-defect detection. The VLSI architecture implements a highly constrained neural network tailored for this specific application: the multi-layer perceptron with strictly local connections. The learning of the weights is performed off line by using the adaptive simulated-annealing algorithm. The neural network has been trained on real plant data: recognition results of the training and classification tasks compare favorably with those obtained by expert human operators. The VLSI architecture receives as input the image (taken on-line on the plant) of a mechanical part and it will find out if at least one structural surface defect is present. The VLSI architecture was optimized, through a set of transformations on the high-level VHDL specifications of the neural network algorithm, to reach real-time operating conditions. Following the proposed approach and the designed architecture, we designed and successfully tested a custom VLSI chip for the real-time implementation of the recognition task.
Year
DOI
Venue
1996
10.1006/rtim.1996.0037
Real-Time Imaging
Keywords
Field
DocType
real-time quality control analysis,industrial plant,vlsi image processing architecture,operant conditioning,real time,adaptive simulated annealing,neural network,chip,image processing,magnetic particle,quality control,multi layer perceptron,visual inspection
Computer vision,Architecture,Computer science,Image processing,Automation,Real-time computing,Chip,Artificial intelligence,VHDL,Artificial neural network,Perceptron,Very-large-scale integration
Journal
Volume
Issue
ISSN
2
6
Real-Time Imaging
Citations 
PageRank 
References 
0
0.34
6
Authors
4
Name
Order
Citations
PageRank
M. Valle19719.19
Luigi Raffo217619.21
Daniele D. Caviglia3278.04
Giacomo M. Bisio4256.24