• reliable visibility estimation using low-cost hardware and custom deep learning models. • construction of a unique synchronized dataset of images and meteorological data. • the proposed multimodal model consistently outperforms all unimodal baselines. • the new multimodal dataset is openly released to support future research. Accurate estimation of atmospheric visibility is essential for numerous safety-critical applications, particularly in the field of transportation. In this study, a deep learning-based approach is investigated using a multimodal input representation that combines RGB images from a fixed-position surveillance camera with tabular meteorological variables collected from a nearby meteorological station. The meteorological input includes temperature, absolute pressure, relative humidity, dew point, wet bulb temperature, average and maximum wind speed, amount of precipitation, solar radiation, and ultraviolet index. Six neural network models for visibility estimation were developed and compared: a multimodal model utilizing both image and tabular meteorological inputs; two ablation models that use only unimodal input (image or meteorological data); a regions-of-interest (ROIs) based model that extracts features from predefined image subregions; and two ablation models that use only a reduced number of meteorological data. The multimodal model uses EfficientNetV2M for feature extraction and a set of fully connected neural networks to integrate the two modalities. The ROIs-based model also uses EfficientNetV2M, but only on manually selected reference regions of the scene. Evaluation was performed on a dataset of 1000 annotated images, with visibility manually determined based on reference points in the scene. The multimodal model achieved a mean squared error of 129,716 m 2 , a mean absolute error of 165.4 m, and an R 2 score of 0.8861, with 84.46 % of predictions falling within a 10 % relative error margin. Although the ROIs-based model slightly outperformed the multimodal model in some regression metrics, its accuracy within tolerance thresholds was lower, and its reliance on manual scene annotation limits scalability. In contrast, the ablation models clearly demonstrated lower performance in almost all evaluated criteria. The results display that the proposed multimodal input strategy provides a balanced and practical approach to automated visibility estimation. Compared to conventional unimodal input models, this architecture offers improved accuracy, stability, and generalisation ability, making it suitable for real-world applications where both visual and environmental data are available.

Coil coating, a technique for applying organic coatings to rolled metal strip substrates, plays a critical role in achieving consistent, high-quality surface finishes. However, these protective coatings are vulnerable to mechanical damage, which can lead to irreversible alterations when exposed to environmental elements. Traditionally, assessing degradation resistance in coil-coated materials involves manual determination of degraded areas. In this study, a classification-based approach to automate this assessment is proposed. Additionally, the selected classification models are compared with semantic segmentation, highlighting their performance and computational efficiency. The results demonstrate that both approaches (classification and semantic segmentation) can assess degradation, with semantic segmentation providing highly accurate results and classification models offering efficient practical deployment alternatives.

Unmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle. However, several issues must be resolved before a stable and reliable group of unmanned vehicles can be generally deployed to solve tasks in civil infrastructures and in industrial facilities. Here, a framework for the guidance of a fleet of unmanned surface vehicles is proposed. The framework utilizes several levels of control, namely Global Planning Level, Local Planning Level, and Low-Level Control. While the individual vehicles are completely autonomous in their operational locomotion and obstacle avoidance (low-level control and local planning), the task assignment for each vehicle (or group of them) is provided by a global planning process, based on the genetic algorithm. The framework provides a concept to solve complex tasks for the fleet of unmanned surface vehicles (USVs). This includes, but is not necessarily limited to, a dynamically changing environment, different types of USVs with special abilities, multiple types of areal restrictions and obstacles, different restrictions for individual USVs, cooperation of multiple USVs to solve their subtasks, energy consumption optimization, etc. The framework can be advantageously applied to tasks such as warehouse logistics, surface maintenance, area exploration, etc. At the end of the study, the application of the framework is presented using a simulated example of cooperative problem solving using six vehicles.

Coil coating is a method of applying an organic coating material to a rolled metal strip substrate in a continuous automated process. It is used to provide a high quality, durable finish to a variety of surfaces. The degradation resistance of coil-coated materials is assessed according to European Standard EN 13523–8 by exposing a coil-coated test specimen to a salt fog at a defined temperature for a defined period of time. After this process, a sample is tested according to the International Organisation for Standardisation ISO 4628 standard to determine the degree of degradation. In this study, a GAN-based technique for automated training set enhancement is proposed to assess the degree of degradation around a scribe. The presented technique is capable of enhancing a manually generated dataset of images with synthetic samples to help refine the performance of the area degradation detector.

Robotic manipulation with nontrivial or irregular objects, which provide various types of grasping points, is of both academic and industrial interest. Recently, a powerful data-driven ASP U-Net deep neural network has been proposed to detect feasible grasping points of manipulated objects using RGB data. The ASP U-Net showed the ability to detect feasible grasping points with exceptional accuracy and more than acceptable inference times. So far, the network has been trained using an Adam optimizer only. However, in order to optimally utilize the potential of ASP U-Net, it was necessary to perform a systematic investigation of suitable training algorithms. Therefore, the aim of this contribution was to extend the impact of ASP U-Net by recommending suitable training algorithms and their parameters based on the result of training experiments.