Search Results (5)

Accurately assessing human thermal comfort plays a key role in improving indoor environmental quality and energy efficiency of buildings. Non-invasive thermal comfort recognition has shown great application potential compared with other methods. Based on thermal correlation analysis, human facial temperature recognition and body thermal adaptive action detection are both performed by one binocular infrared camera. The YOLOv5 algorithm is applied to extract facial temperatures of key regions, through which the random forest model is used for thermal comfort recognition. Meanwhile, the Mediapipe tool is used to detect probable thermal adaptive actions, based on which the corresponding thermal comfort level is also assessed. The two results are combined with PMV calculation for multivariate human thermal comfort prediction, and a weighted fusion strategy is designed. Seventeen subjects were invited to participate in experiments for data collection of facial temperatures and thermal adaptive actions in different thermal conditions. Prediction results show that, by using the experiment data, the overall accuracies of the proposed fusion strategy reach 82.86% (7-class thermal sensation voting, TSV) and 94.29% (3-class TSV), which are better than those of facial temperature-based thermal comfort prediction (7-class: 78.57%, 3-class: 90%) and PMV model (7-class: 20.71%, 3-class: 65%). If probable thermal adaptive actions are detected, the accuracy of the proposed fusion model is further improved to 86.8% (7-class) and 100% (3-class). Furthermore, by changing clothing thermal resistance and metabolic level of subjects in experiments, the influence on thermal comfort prediction is investigated. From the results, the proposed strategy still achieves better accuracy compared with other single methods, which shows good robustness and generalization performance in different applications. Full article

This paper introduces a vision-based target recognition and positioning system for UAV mobile landing scenarios, addressing challenges such as target occlusion due to shadows and the loss of the field of view. A novel image preprocessing technique is proposed, utilizing finite adaptive histogram equalization in the HSV color space, to enhance UAV recognition and the detection of markers under shadow conditions. The system incorporates a Kalman filter-based target motion state estimation method and a binocular vision-based depth camera target height estimation method to achieve precise positioning. To tackle the problem of poor controller performance affecting UAV tracking and landing accuracy, a feedforward model predictive control (MPC) algorithm is integrated into a mobile landing control method. This enables the reliable tracking of both stationary and moving targets via the UAV. Additionally, with a consideration of the complexities of real-world flight environments, a mobile tracking and landing control strategy based on airspace division is proposed, significantly enhancing the success rate and safety of UAV mobile landings. The experimental results demonstrate a 100% target recognition success rate and high positioning accuracy, with x and y-axis errors not exceeding 0.01 m in close range, the x-axis relative error not exceeding 0.05 m, and the y-axis error not exceeding 0.03 m in the medium range. In long-range situations, the relative errors for both axes do not exceed 0.05 m. Regarding tracking accuracy, both KF and EKF exhibit good following performance with small steady-state errors when the target is stationary. Under dynamic conditions, EKF outperforms KF with better estimation results and a faster tracking speed. The landing accuracy is within 0.1 m, and the proposed method successfully accomplishes the mobile energy supply mission for the vehicle-mounted UAV system. Full article

Camouflage is a natural or artificial process that prevents an object from being detected, while camouflage breaking is a countering process for the identification of the concealed object. We report that a perfectly camouflaged object can be retrieved from the background and detected with stereo-vision-assisted three-dimensional (3D) imaging. The analysis is based on a binocular neuron energy model applied to general 3D settings. We show that a perfectly concealed object with background interference can be retrieved with vision stereoacuity to resolve the hidden structures. The theoretical analysis is further tested and demonstrated with distant natural images taken by a drone camera, processed with a computer and displayed using autostereoscopy. The recovered imaging is presented with the removal of background interference to demonstrate the general applicability for camouflage breaking with stereo imaging and sensing. Full article

Direct laser deposition (DLD) requires high-energy input and causes poor stability and portability. To improve the deposited layer quality, conducting online measurements and feedback control of the dimensions, temperature, and other melt-pool parameters during deposition is essential. Currently, melt-pool dimension measurement is mainly based on machine vision methods, which can mostly detect only the deposition direction of a single melt pool, limiting their measurement range and applicability. We propose a binocular-vision-based online measurement method to detect the melt-pool width during DLD. The method uses a perspective transformation algorithm to align multicamera measurements into a single-coordinate system and a fuzzy entropy threshold segmentation algorithm to extract the melt-pool true contour. This effectively captures melt-pool width information in various deposition directions. A DLD measurement system was constructed, establishing an online model that maps the melt-pool width to the offline deposited layer width, validating the accuracy of the binocular vision system in measuring melt-pool width at different deposition angles. The method achieved high accuracy for melt-pool measurements within certain deposition angle ranges. Within the 30°–60° measurement range, the average error is 0.056 mm, with <3% error. The proposed method enhances the detectable range of melt-pool widths, improving cladding layers and parts. Full article

As a promising method for moiré processing, sampling moiré has attracted significant interest for binocular vision-based 3D measurement, which is widely used in many fields of science and engineering. However, one key problem of its 3D shape measurement is that the visual angle difference between the left and right cameras causes inconsistency of the fringe image carrier fields, resulting in the phase mismatch of sampling moiré. In this paper, we developed a phase correction method to solve this problem. After epipolar rectification and carrier phase introduction and correction, the absolute phase of the fringe images was obtained. A more universal 3D sampling moiré measurement can be achieved based on the phase match and binocular vision model. Our numerical simulation and experiment showed the high robustness and anti-noise ability of this new 3D sampling moiré method for high-precision 3D shape measurement. As an application, cantilever beams are fabricated by directed energy deposition (DED) using different process parameters, and their 3D deformation caused by residual stresses is measured, showing great potential for residual stress analyses during additive manufacturing. Full article