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.