Jacek Lubczonek

The number of CNN application areas is growing, which leads to the need for training data. The research conducted in this work aimed to obtain effective detection models trained only using simplified synthetic objects (SSOs). The research was conducted on inland shallow water areas, while images of bottom objects were obtained using a UAV platform. The work consisted in preparing SSOs, thanks to which composite images were created. On such training data, 120 models based on the YOLO (You Only Look Once) network were obtained. The study confirmed the effectiveness of models created using YOLOv3, YOLOv5, YOLOv8, YOLOv9, and YOLOv10. A comparison was made between versions of YOLO. The influence of the amount of training data, SSO type, and augmentation parameters used in the training process was analyzed. The main parameter of model performance was the F1-score. The calculated statistics of individual models indicate that the most effective networks use partial augmentation, trained on sets consisting of 2000 SSOs. On the other hand, the increased transparency of SSOs resulted in increasing the diversity of training data and improving the performance of models. This research is developmental, and further research should improve the processes of obtaining detection models using deep networks.

Consumer-grade graphics processing units (GPUs) offer a potentially affordable and energy-efficient alternative to enterprise-class hardware for real-time image processing tasks, but systematic multi-criteria analyses of their suitability remain rare. This article fills that gap by evaluating the performance, power consumption, and cost-effectiveness of GPUs from three leading vendors, AMD, Intel, and Nvidia, in an inland water transport (ITW) context. The main objective is to assess the feasibility of using consumer GPUs for deep learning tasks involving navigational sign detection, a critical component for ensuring safe and efficient inland transportation. The evaluation includes the use of image datasets of inland water transport signs processed by widely used detector and classifier models such as YOLO (you only look once), ResNet (residual neural network l), and MobileNet. To achieve this, we propose a multi-criteria framework based on a weighted scoring method (WSM), covering 21 different characteristics such as compatibility, resting power, energy efficiency in learning and inference, and the financial threshold for technology adoption. The results confirm that consumer-grade GPUs can deliver competitive performance with lower initial costs and lower power consumption. The findings underscore the enduring value of our analysis, as its framework can be adapted for ongoing comparisons of evolving GPU technologies using the proposed methodology.

The aim of this study was to develop a comprehensive methodology for assessing the effectiveness of conceptual navigation maps. To achieve this, a set of indicators was developed to simplify and standardise the evaluation process. A key challenge in analysing the effectiveness of maps is the variety of ways to assess their effectiveness (eye-tracking measures, visual interpretation, questionnaires), which often leads to ambiguous interpretations. In the initial phase, three separate indicators were established: readability of the map, correctness of map symbol identification, and the time required to identify these objects. For the readability indicator, a correlation analysis with eye-tracking measurements and a heatmap decomposition was carried out, which partly reduced the complexity of the data. This led to the development of a single universal indicator, representing the overall effectiveness of the map in numerical form and allowing a simple comparison of maps. Based on the methodology developed, we were able to conclude that the designed 3D concept maps are more effective than their 2D counterparts. The methodology and universal indicator presented in this study can be applied in practice to evaluate entire series of concept maps and provide designers—including those outside academia—with indicator-based tools to evaluate the effectiveness of maps.

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters.

This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods.

Coastal zone areas are important parts of the environment, very often subject to constant change

This paper presents a method for integrating data acquired by unmanned surface vehicles and unmanned aerial vehicles. The aim of this work was to create a uniform bathymetric surface extending to the shoreline. Such a body of water is usually characterized by ultra-shallow depths, which makes measurement impossible even with hydrographic autonomous vessels. Bathymetric data acquired by the photogrammetric method are, however, characterized by large errors with increasing depth. The presented method is based on processing of two data sets using a bathymetric reference surface and selection of points on the basis of generated masks. Numerical bathymetric models created by interpolation methods confirmed the usefulness of the concept adopted.

Coastal regions are susceptible to rapid changes as they constitute the boundary between the land and the sea

The research in this paper is concerned with the development of a continuous elevation model in the coastal zones of inland waters. The source data for the creation of numerical terrain models were data acquired by contemporary sensors, such as such as a single-beam echosounder and an unmanned aircraft system. Different interpolation methods were tested in the study. A new approach in the research field is an interpolation method based on the processing of datasets with different degrees of spatial data reduction. The authors call it the Spatial Interpolation Method based on Data Reduction (SIMDR). The choice of method is based on quantitative and qualitative analysis, taking into account the type of interpolation and the method of geodata reduction. A proposal for the practical implementation of the method involves script processing, which automates the processes of modeling and error calculation.