Deep learning architectures for location and identification in storage systems

Abstract

This document exposes the application of two deep learning models based on ResNet-18 architectures, intended for the location and identification of products in storage areas. One model obeys a tree structure and the other a structure under an ouroboron cycle. The performance of both models is evaluated using the metrics of training time, processing time and level of learning precision, which allows recommendations to be made regarding which one should be used for order preparation purposes, based on multilevel feature extraction. The total training time of the first model is 34.65 minutes and the second 40.43 minutes. The analysis of results allowed the detection parameters to be adjusted, finally with the refined models, through confusion matrices, precision results greater than 90% and processing times are obtained, which for model 1 is 6.8565 seconds and for model 2 is 4.884 seconds. For practical purposes, training times are not relevant, as are the precision and processing times for selecting the most convenient model according to the end user's objectives.