This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10–20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Universal models that facilitate the detection of objects of diverse categories in images obtained through optical remote sensing (ORS) hold the greatest practical value. The objective of this research was to develop models that enable accurate detection of objects belonging to five different categories, namely: aircraft, airport, helicopter, oil tank, and warship. These selected categories of objects are frequently analysed in both civil and military applications. This article focuses on developing solutions for object detection in ORS images using the YOLOv8 architecture. To avoid the problem of missing and false detections, algorithms such as Soft Non-Maximum Suppression (Soft-NMS) and the Confidence Propagation Cluster (CPC) were introduced into the YOLOv8 model. A data transfer technique was employed to enable the models to learn more quickly and generalise the acquired knowledge more effectively. Another approach involved freezing selected layers. This reduces overfitting when training with domain-specific datasets and decreases memory usage. The research also compares the results obtained using modern neural network architectures such as YOLOv5, RTMDet and DETR. Proposing new architectures and solutions alone is not enough to improve object detection on an imbalanced training set. Therefore, this research also focused on preparing an appropriate dataset to enable the effective training of applied models. In particular, the training dataset should include a wide variety of challenging scenes, such as densely packed objects and complex backgrounds, as well as objects of different categories and sizes within a single image. Consequently, a new dataset called DOTANA was created based on two existing, publicly available datasets: the Large-Scale Dataset for Detecting Objects in Aerial Images (DOTA) and the Remote Sensing Dataset for Geospatial Object Detection (RSD-GOD). The proposed solutions produce better results than previous studies for objects of the same categories from the RSD-GOD dataset. The best average precision metric (mAP) values obtained are mAP$_{50}$=0.939 and mAP$_{50-95}$=0.606 using the YOLOv8 model and the Soft-NMS algorithm.