Parts classification in assembly lines using multilayer feedforward neural networks

The paper describes a low cost system for a position, scale, and rotation invariant classification of mechanical parts in assembly lines using multilayer feedforward neural networks. After image acquisition, moment invariants are calculated for each significant region in the input image. Different network sizes were tested for classifying these features and the authors compare these results with the traditional k-nearest neighbor (k-NN), for different k values. Hybrid strategies were adopted for training the networks. They used deterministic methods, such as conjugate gradient and Levenberg-Marquardt algorithms, combined with a stochastic method, simulated annealing. The system deals with digital images with an unknown number of unoccluded object types and poses. Results show that, in this case, artificial neural networks had better generalization capability than k-NN; despite geometrical transformations and other degradations over the images. The systems runs on low cost personal computers and can therefore be easily adapted for use even by small factories.