Development of a Software and Hardware Complex for Monitoring Processes in Production Systems

TL;DR

This paper describes a hardware-software system that improves production monitoring by using advanced algorithms for object detection.

Contribution

A novel cascade algorithm using YOLO and VGG19 for improved detail detection in production systems.

Findings

The developed system achieved 93% accuracy in detail detection.

The traditional approach only reached 72% accuracy.

The cascade algorithm reduced the training sample size for neural networks.

Abstract

The article presents a detailed exposition of a hardware–software complex that has been developed for the purpose of enhancing the productivity of accounting for the state of the production process. This complex facilitates the automation of the identification of parts in production containers and the utilisation of supplementary markers. The complex comprises a mini computer (system unit in industrial version) with connected cameras (IP or WEB), a communication module with LED and signal lamps, and developed software. The cascade algorithm developed for the detection of labels and objects in containers employs trained convolutional neural networks (YOLO and VGG19), thereby enhancing the recognition accuracy while concurrently reducing the size of the training sample for neural networks. The efficacy of the developed system was assessed through laboratory experimentation, which yielded experimental results demonstrating 93% accuracy in detail detection using the developed algorithm, in comparison to the 72% accuracy achieved through the utilisation of the traditional approach employing a single neural network.

Figures