Detection of stress and the development of innovative platforms for stress monitoring have attracted significant attention in recent years due to the growing awareness of the harmful effects of stress on mental and physical health. Stress is a widespread issue affecting individuals and often goes unnoticed as a health concern. It can lead to various negative physiological conditions, including anxiety, depression, cardiovascular diseases and cognitive impairments. The aim of this paper is to provide an overview of studies focusing on embedded devices for non-invasive stress detection, primarily in the form of a modified computer mouse or keyboard. This study not only fills a critical gap in the literature but also provides valuable insights into the design and implementation of hardware-based stress-detection methods. By focusing on embedded devices, specifically computer peripherals, this research highlights the potential for integrating stress monitoring into everyday workplace tools, thereby offering practical solutions for improving occupational health and well-being.

This paper presents the development of a robotic workstation that integrates a collaborative robot as an assistant, leveraging advanced computer vision techniques to enhance human–robot interaction. The system employs state-of-the-art computer vision models, YOLOv7 and YOLOv8, for precise tool detection and gesture recognition, enabling the robot to seamlessly interpret operator commands and hand over tools based on gestural cues. The primary objective is to facilitate intuitive, non-verbal control of the robot, improving collaboration between human operators and robots in dynamic work environments. The results show that this approach enhances the efficiency and reliability of human–robot cooperation, particularly in manufacturing settings, by streamlining tasks and boosting productivity. By integrating real-time computer vision into the robot’s decision-making process, the system demonstrates heightened adaptability and responsiveness, creating the way for more natural and effective human–robot collaboration in industrial contexts.

The automated detection of individuals within vehicles, with minimal to no human intervention, hold multifaceted implications in contemporary contexts. These applications span from aiding emergency responders and optimising transportation networks to facilitating automated crash response mechanisms and enforcing regulations concerning High Occupancy Vehicle and High Occupancy Toll lanes. In this paper, we introduce our camera system designed for passenger counting, leveraging five IP cameras equipped with a range of optical filters, employing image registration techniques, and integrating the YOLOv8 object detection model. The Surveillance Camera Array Network (SCAN) operates within the near-infrared domain of the electromagnetic spectrum in conjunction with the visible part. Four VIVOTEK IP cameras are outfitted with near-infrared, neutral density, polarising, and ultraviolet optical filters, while the final camera retains its stock lens. Our primary challenge lies in managing variable lighting conditions throughout the day. However, during nighttime, we achieve nearly perfect image capture of vehicles. To mitigate noise, glare, and other impediments, we initially apply camera calibration, image preprocessing, cropping, image registration, and finally, image fusion. Our findings demonstrate that our cost-effective SCAN system adeptly detects passengers in cars equipped with window tinting. The results obtained during testing conditions resulted in an 66% true positive rate, 8% false positive rate and 26% false negative rate within best dataset. Additionally, we provide created datasets displaying passengers inside Suzuki Vitara, Jaguar XF, and Honda CRV vehicles with various levels of window tinting, to facilitate future community endeavors in addressing this challenging task.

The forest constitutes an essential and irreplaceable component of life for all organisms, with its primary significance lying in its role in creating a breathable atmosphere on Earth. Forests are vital for human health and well-being and hold significant ecological and economic value for humanity. This study aims to propose a method for identifying forest stands using artificial intelligence techniques. A custom dataset was developed, comprising high-quality satellite images that capture various structures such as forests, fields, roads, buildings, and lakes. This dataset was employed to train models from the category of convolutional neural networks that operate on the principle of instance segmentation. Several models, including YOLOv8, YOLOv5 and Mask R-CNN, were tested and compared. An optimal model was selected based on parameters such as detection accuracy, total training time, and the precision of labeling detected image elements. The selected model was then evaluated using images not included in the original training dataset to simulate real-world deployment scenarios. The final accuracy of best model achieved 91.67%. This model can detect the presence of forest stands in satellite images, as well as other features such as roads, buildings etc. The proposed method offers potential benefits for forest technicians, who can integrate it with other methods to monitor forest cover effectively.

Traffic accident detection and object detection have become key areas of research due to their direct impact on safety, traffic congestion mitigation, and intelligent traffic planning. This study presents a structured analysis of classical detection methods and artificial intelligence-based techniques, highlighting their methodologies, objectives, and performance results. The study categorizes existing research into threshold-based approaches, statistical approaches, image processing, rule-based approaches, and machine learning approaches, with further emphasis on predictive modeling, graph-based approaches, and optimization approaches. Considerable emphasis is placed on identifying systems that are capable of operating under adverse weather conditions such as fog, rain, and snow. These scenarios significantly affect detection accuracy. Although several authors incorporate environmental resilience into their models, most studies still evaluate performance under ideal conditions, revealing a critical gap in research. This analysis highlights the need to develop robust detection mechanisms that can adapt to real-world variability and environmental disturbances. Findings show that AI-based methods significantly outperform classical approaches in terms of adaptability and scalability, but their dependence on training data limits their performance in adverse conditions. The study concludes with recommendations for future work to prioritize multimodal sensing, generalization across weather conditions, and integration of environmental intelligence to ensure reliable real-time detection of traffic events under all operating conditions.