CNN-Based Terrain Classification with Moisture Content Using RGB-IR Images

Unmanned mobile robots in rough terrains are a key technology for achieving smart agriculture and smart construction. The mobility performance of robots highly depends on the moisture content of soil, and past few studies have focused on terrain classification using moisture content. In this study, we demonstrate a convolutional neural network-based terrain classification method using RGB-infrared (IR) images. The method first classifies soil types and then categorizes the moisture content of the terrain. A three-step image preprocessing for RGB-IR images is also integrated into the method that is applicable to an actual environment. An experimental study of the terrain classification confirmed that the proposed method achieved an accuracy of more than 99% in classifying the soil type. Furthermore, the classification accuracy of the moisture content was approximately 69% for pumice and 100% for dark soil. The proposed method can be useful for different scenarios, such as small-scale agriculture with mobile robots, smart agriculture for monitoring the moisture content, and earthworks in small areas.

Scene terrain classification for autonomous vehicle navigation based on semantic segmentation method

Autonomous transportation is a new paradigm of an Industry 5.0 cyber-physical system that provides a lot of opportunities in smart logistics applications. The safety and reliability of deep learning-driven systems are still a question under research. The safety of an autonomous guided vehicle is dependent on the proper selection of sensors and the transmission of reflex data. Several academics worked on sensor-based difficulties by developing a sensor correction system and fine-tuning algorithms to regulate the system’s efficiency and precision. In this paper, the introduction of vision sensor and its scene terrain classification using a deep learning algorithm is performed with proposed datasets during sensor failure conditions. The proposed classification technique is to identify the mobile robot obstacle and obstacle-free path for smart logistic vehicle application. To analyze the information from the acquired image datasets, the proposed classification algorithm employs segmentation techniques. The analysis of proposed dataset is validated with U-shaped convolutional network (U-Net) architecture and region-based convolutional neural network (Mask R-CNN) architecture model. Based on the results, the selection of 1400 raw image datasets is trained and validated using semantic segmentation classifier models. For various terrain dataset clusters, the Mask R-CNN classifier model method has the highest model accuracy of 93%, that is, 23% higher than the U-Net classifier model algorithm, which has the lowest model accuracy nearly 70%. As a result, the suggested Mask R-CNN technique has a significant potential of being used in autonomous vehicle applications.

Adaptive online terrain classification method for mobile robot based on vibration signals

To improve the accuracy of terrain classification during mobile robot operation, an adaptive online terrain classification method based on vibration signals is proposed. First, the time domain and the combined features of the time, frequency, and time–frequency domains in the original vibration signal are extracted. These are adopted as the input of the random forest algorithm to generate classification models with different dimensions. Then, by judging the relationship between the current speed of the mobile robot and its critical speed, the classification model of different dimensions is adaptively selected for online classification. Offline and online experiments are conducted for four different terrains. The experimental results show that the proposed method can effectively avoid the self-vibration interference caused by an increase in the robot’s moving speed and achieve higher terrain classification accuracy.

Research on Micro-terrain Classification of Transmission Line Galloping

Micro-terrain and micro-weather have an important impact on transmission line galloping. In order to carry out galloping prediction of micro-terrain, the classification of galloping micro-terrain is studied in this work. Firstly, we collect historical data of 1537 galloping points from the State Grid Corporation of China, and select 208 galloping points located in the micro-terrain area by analyzing the altitude and the topographic relief characteristics around each galloping point. Then the galloping micro-terrain types are extracted by Empirical Orthogonal Function method, the first four spatial modes of galloping micro-terrain are the windward slope of east-west mountain area, the windward slope of north-south mountain area, the independent hill, and the saddle back of mountain/hill. Finally, the regional characteristics of typical micro-terrain are analyzed according to the actual lines.

ECOLOGICAL GEOMORPHOLOGY, ITS SUBJECT, HISTORICAL ASPECTS AND METHODS

Paper considers ecological geomorphometry as the scientific area aimed at the study of place and functions of terrain and modern morphogenetic processes in the functioning of other components of natural environment, ecosystems, and in shaping of the conditions for human activities, applying the methods of quantitative spatial analysis. Some terminological issues are considered, as well as a short history of geomorphometry, its main tasks and research methods. In particular, the methods of quantitative analysis of the structure of terrain surface are considered, namely –the detection of the spatial trends, of periodicity, and of the spatial autocorrelation. The capabilities of the method of autocovariogram building and analysis are shown for the purpose of the studies of terrain elements, forms and types, their automatic delineation and classification. The basics of ecologically grounded classification of morphometric variables are considered, as well as the principles of the delineation of complex morphometric variables (topographic ecological indices), which reflect the impact of terrain morphology on ecological processes and ecological factors distributions. The main principles of ecological classification of terrain elements are also considered, together with the automatic delineation of terrain forms and types on the basis of their geometric signatures, that are defined through the distribution of the set of morphometric variables and the parameters of their spatial variability. Paper also reviews former studies by the author in the areas of morphometric analysis of the terrain surfaces of several study areas in Ukrainian Carpathians; the automatic terrain classification and segmentation; the analysis of the relationships between morphometric variables and ecological factors, the character of ground cover and the vegetation. Key words: ecological geomorphometry; topographic surface; morphometric variables; morphotop, autocovariogram; geometric signature.