This paper presents an investigation of the development of a real-time object tracking system that utilizes computer vision techniques and incorporates pan-tilt control mechanisms. The system architecture includes hardware components such as a camera and a pan-tilt mechanism, and software components such as object detection and tracking algorithms. To achieve efficient and accurate tracking, the Kalman filter, which uses a state-space model, was included in the object tracking process. The proposed system is implemented and evaluated through experiments to demonstrate its feasibility and effectiveness in real-world scenarios. The results indicate that the proposed system is capable of achieving real-time and accurate object tracking, demonstrating its potential for practical applications in various fields.

The object of the research is in-pipe defect detection and classification. The primary problem to be solved is the inefficiency, high cost, and inaccuracy of traditional manual inspection methods, which are often time-consuming and prone to human error. The results obtained include the creation of a multi-modal platform that integrates Red-Green-Blue (RGB) imaging and depth data with advanced artificial intelligence algorithms, Canny edge detection, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering, achieving a 93 % mean Average Precision (mAP) in detecting and classifying various defects such as cracks, corrosion, and debris. A brief interpretation of the findings reveals that the high performance is due to the synergy between multi-modal sensing, artificial intelligence pattern recognition, and robust robotic navigation. This integrated approach ensures that the system not only detects defects accurately but does so in real time. Features and characteristics of the obtained results that directly address the identified problem include real-time high-precision defect identification, and reduced inspection downtime. As a result, inspection time is shortened, costs are lowered, and the safety of the pipeline system is increased, leading to accurate measurement of indicators (93 % mAP) and a reduction in occupational safety risks. The developed system is designed for use in traditional industrial environments, especially in large pipeline networks and in conditions where traditional methods are ineffective

Museum and archive storage facilities worldwide face persistent difficulties in maintaining stable microclimatic conditions. This study presents a Smart Archival Box, a modular hardware–software system for preventive conservation, together with a model-based evaluation of its microclimate control. The numerical domain has a nominal volume of 0.125 m³ (0.5 m × 0.5 m × 0.5 m) and combines a 1 kg ArtSorb cassette, four TEC1-12706 thermoelectric modules, particulate/activated-carbon filtration, distributed monitoring and RFID-based access control. Temperature is regulated by a single proportional–integral–derivative (PID) controller with ambient-load feedforward, back-calculation anti-windup, hot-side derating and a dew-point safety limiter. Three scenarios—hybrid (H), active (A) and passive (P)—were evaluated over 10,173 one-second intervals (10,174 records; 2.8258 h) while the prescribed ambient temperature rose from 16 to 32 °C, and ambient relative humidity fell from 67% to 27%. H and A remained within the broader 16–20 °C preservation band for 100% of the run and within the tighter 17.5–18.5 °C control band for 79.7% and 80.5%, respectively, finishing at 18.53 °C and 18.56 °C. P reached 21.20 °C and remained within the tighter band for 47.7% of the run. Calculated electrical consumption was 0.05177 kWh for H, 0.05156 kWh for A and 0.00565 kWh for P. Relative humidity remained within 45–55% in all scenarios. The filtered H and A scenarios reduced mean PM2.5 and total volatile organic compound (TVOC) exposure by 98.4% and 94.4%, respectively, relative to the prescribed ambient profiles. The graphs for document prototype construction and functional bench observations and model results that have not yet been validated by enclosure tests are presented in Results Section. The close H/A performance over this short disturbance does not establish an advantage of ArtSorb and requires repeated humidity-disturbance experiments.

Pipeline integrity and safety are critical for transporting water, oil and gas, but traditional inspection methods are resource-intensive and error-prone, delaying defect detection. The objective of this work is to create an autonomous robotic system capable of detecting, localizing and classifying problems inside a pipe using sophisticated imaging techniques and artificial intelligence algorithms. To achieve this goal, multimodal precision-enhancing sensors (high-resolution RGB cameras, ultrasonic and infrared sensors) with data processing methods such as the Canny edge detector and the DBSCAN clustering algorithm were used. The research approaches include the creation of a modular robotic platform for autonomous navigation, the creation of synthetic data for training deep neural networks, and experimental validation on pipelines of different materials and dimensions. The experimental results show that the system outperforms existing approaches, making it a valuable tool for predictive maintenance, regulatory compliance and improving pipeline safety.

The SLAM algorithms used on inexpensive compact boards in many standalone mobile devices are not fast enough. To solve this problem, we modified the RTAB-Map method in order to increase its speed without losing the quality of the algorithm itself. One way to improve the speed of the algorithm is to replace the SIFT keypoint extraction algorithm. In this article, we made a comparative analysis of methods for identifying local features from images.