AGV Detection in Industrial Environments through Computer Vision

• Wilson Eduardo Barioni UTFPR
• Igor Pardal Latini UTFPR
• André Lazzaretti UTFPR
• Marco Teixeira UTFPR
• Flavio Neves UTFPR
• Lucia Valeria Ramos De Arruda UTFPR

Abstract

Automated Guided Vehicles (AGVs) are usual in industrial settings, with an increasing economic impact on processes. They move through numerous environments inside factories, commonly navigating long distances, while performing several activities. The constant detection of such vehicles, especially in cases of maintenance and safety, is a main issue in an industry setting. Usually, this information could be provided by a supervisory system, but many applications are not so large as to make such a system viable. Thus, a solution via machine learning and computer vision is developed in this work, by using simple cameras, such as the factories' security cameras. As the industrial environment is a scenario with a lot of variation and noise, the Transfer Learning technique is used to improve the training step of the developed AGV detection system. Finally, a database with 1067 images is used to build and validate the model, achieving a result greater than 80% of F1-score for various confidence values.

Keywords: Training, Computer vision, Remotely guided vehicles, Computational modeling, Transfer learning, Cameras, Production facilities, AGV, YOLOv5, object detection, computer vision, machine learning