Classification is the key task of deep learning. Among others, it is used in computer vision for object recognition, and in natural language processing for the masked language modeling. All of these tasks are crucial for modern automatic and semi-automatic production facilities powered by Automated Guided Vehicles (AGVs), because they enable intelligent inspection, maintenance log analysis and operation control. Especially in such safety-critical domains, in which humans and machines often coexist, it is crucial to provide new methods that could help us understand the operation of these black-boxes. That is why we present a novel visual-analytic methods that can be used to examine how networks perceive relations/similarities between the known classes. Our methods operate solely on the trained models and do not require any data samples. They can also reveal some quality issues in the training datasets and indicate low model accuracy. Our methods are suitable for generic vision and language models, but can also be used in transfer learning scenarios. To empirically validate our approach in such a scenario, we conduct experiments on a state-of-the-art mobile vision model - MobileNetV2 - fine-tuned for vehicle classification. We release a new dataset - UtilityVehicles - featuring images of various vehicles that can occur in industry. The presented use case is a vision-based application for Augmented Reality applications for smartphones and embedded devices for AGVs in automatic and semi-automatic production facilities. We release a GitHub repository with data and code: https://github.com/iitis/UtilityVehicles.

Automated Ground Vehicles (AGVs) use deep-learning-based vision systems to perceive the surrounding environment and extract relevant information about it. Although deep learning models offer high capabilities, they require large amounts of data to be properly trained and tested. Testing is especially important when off-the-shelf models are used by the AGVs - to examine whether they can meet the demands of complex environments such as the production halls of automated factories. One area of such perception algorithms is object recognition. To test such systems, we propose a solution based on ArUco fiducial markers used for automatic labeling of objects. Our solution can be used to test deep learning systems in real time directly on a robot. Our solution requires minimal interference with the environment and additional infrastructure - the desired objects only need to be marked with a marker printed on a home printer. Therefore, the presented testing procedure can be used for testing of AGVs in real-life environments during a real ride from an actual robot perspective. Data gathered during the online testing can be used for the offline comparison of the accuracy of different deep learning models. Although we focus on the online and offline testing in our study, we also incorporated a marker masking procedure. Therefore, the resulting datasets may also be used for training.

This article presents the comparative analysis of various JavaScript libraries, providing the possibilities of web-based geo-visualization, that can be applied in the case of AGV (Automated Guided Vehicle) working in the open area using GNSS (Global Navigation Satellite Systems) localization systems. A representative group of JS libraries has been selected for analysis. We focused on libraries that enable map generation with the use of the Scalable Vector Graphics format. Their performance issues and development potential are compared.