Search Results (165)

Accurate oil–water interface measurement in small transparent test tubes is important for subsequent volume readout in laboratory analysis. However, manual observation and conventional vision-based methods are easily affected by illumination variation, wall stains, and bubbles, while deep learning detectors alone usually provide only coarse semantic perception. To address this issue, a coarse-to-fine framework is proposed for robust oil–water interface measurement. In the coarse stage, YOLOv8n is used to provide semantic constraints for subsequent processing. In the fine stage, a Fisher-discriminative chromatic-weighted brightness feature is constructed from RGB information, where the RGB weights are derived from the Fisher criterion to enhance oil–water chromatic separability rather than using fixed grayscale or empirical channel weights. This feature is then fused with a SobelY-based vertical-gradient feature to improve interface localization. A stain-aware row-aggregation strategy with effective-pixel compensation is further introduced to suppress artefact interference. The validated interface position is finally converted into a volume readout, with additional correction for bubble-induced bias. The framework was validated on sampled frames from a complete shale-oil core pressing process conducted under mixed-lighting conditions. Stage-wise evaluation and ablation results indicate that the proposed design improves readout stability under stains, bubbles, and illumination variation, achieving a mean absolute error of 0.0159 mL and keeping the maximum error below 0.03 mL in the current experimental setup. Full article

Age-related changes in visual perception alter attentional deployment, yet computational models of visual attention have been validated almost exclusively on younger populations. This limits both the theoretical investigation of age-specific mechanisms and practical applications in age-inclusive design, where researchers depend on specialised eye-tracking equipment to observe such differences. Therefore, we present the Elderly Visual Attention Estimation (EVAE) model, a computational framework that predicts early visual attentional orienting in older adults by combining stimulus-driven image features with age-specific top-down priors. The framework models six dimensions of elderly visual attention from cross-age eye-tracking data: colour brightness sensitivity, centre bias, foreground–background differentiation, depth detection, early attentional prior, and sustained-attention spatial prior. On public datasets, EVAE achieves an AUC-Judd of 0.92, which outperforms existing saliency models and deep learning approaches such as DeepGaze II. The framework is optimised for an input resolution of 128 × 96 pixels, producing fixation probability maps that are upsampled to match the original stimulus resolution for practical interface evaluation. Cross-age validation confirms the model’s specificity, as EVAE predicts attentional behaviour in older adults but does not generalise to younger adults. An ablation study shows that image features and top-down spatial priors each contribute independently to prediction accuracy, and that bottom-up saliency alone cannot account for age-related attentional patterns. Centre bias and early attentional prior are the strongest predictors, indicating that visual ageing involves greater reliance on spatial strategies and compensatory processing. As an alternative to hardware-based eye-tracking, EVAE widens the scope of empirical research into older adults’ visual attention and informs the design of accessible digital interfaces. Full article

The light environment in orchards is highly heterogeneous, characterized by dappled light caused by sunlight filtering through plant canopies, and poses a challenge for the color perception of insects. For the crepuscular moth Grapholita molesta, a global pest of fruit trees, females can visually discriminate between plants to locate young leaves with higher brightness or intensity for oviposition around dusk. However, whether dappled light affects this brightness discrimination performance remains unknown. In this study, we investigated the oviposition preference of G. molesta females between two brightness stimuli (light-green and dark-green), as well as among three different light intensities under uniform light, simulated dappled light, and complex dappled light conditions at three ecologically relevant illuminances (100, 1, and 0.01 lx). G. molesta females laid significantly more eggs on light-green than dark-green stimuli under all three light conditions at each illuminance. Similarly, females consistently preferred high-intensity over lower-intensity light areas across uniform, dappled, and complex dappled light conditions, with more than 66% oviposition frequency. Our findings show that the brightness and intensity discrimination ability of G. molesta is not affected by dappled light, which may enable moths to accurately locate suitable oviposition sites even when the orchard canopy creates a heterogeneous light distribution. Full article

Due to dust, haze and uneven lighting conditions, images captured in coal mines frequently suffer severe quality degradation. Traditional dehazing methods typically overlook color characteristics and employ single algorithms, and deep-learning-based approaches require substantial training data and demand high hardware specifications, which restricts their dehazing performance and efficiency. This research proposes an efficient image dehazing framework. This method integrates bright and dark channel information to derive contrast feature values based on their linear differences. These values reflect dust concentration levels in the environment. By incorporating dust sensor data, the adaptive scaling coefficient and dust compensation terms are established. The adaptive scaling coefficient serves as a dynamic pixel selection ratio during ambient light estimation, effectively preserving the brightest pixel points. The global color mean functions as the criterion for determining image color characteristics, distinguishing between color images and low-light grayscale images to enable different dehazing approaches. This process achieves state verification and information complementarity between visual perception and dust measurement. The weighted fusion of bright and dark channels yields more accurate estimation for ambient light and transmission. Additionally, a weighted guided filter is designed with dust compensation terms incorporated. Ablation studies were conducted to validate the effectiveness of this method in enhancing image features. Finally, comparative experiments were performed using a self-constructed coal mine hazy image dataset, along with SOTS-indoor and SOTS-outdoor datasets. Experimental results demonstrate that, compared with other state-of-the-art methods, this method effectively removes haze while restoring image features and details, exhibiting superior stability, adaptability, and computational efficiency. Full article

Under complex lighting conditions, particularly in low-light environments, general object detectors often suffer from degraded detection performance due to insufficient brightness, severe noise, and loss of discriminative details. This issue is especially critical in underground mining scenarios, where weak illumination, complex backgrounds, dust interference, and frequent small or partially occluded targets make reliable visual perception highly challenging. To address this issue, we propose an Illumination-Aware Detection Network (IADNet) for object detection in low-light environments. Specifically, an Illumination Modeling Subnetwork (IMS) is designed to extract illumination-aware and degradation-aware auxiliary features from low-light images. Within the IMS, an Adaptive Weighted Downsampling (AWD) layer is introduced to reduce noise interference during feature downsampling and enhance illumination-aware representation learning. Furthermore, a Global Feature Enhancement Module (GFEM) is incorporated to strengthen global context modeling and improve feature representation capability in complex scenes. In addition, an extra contrastive loss is introduced to constrain the optimization of the IMS, and weighting factors are employed to balance the detection loss and the contrastive loss during training. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method. On the public ExDark dataset, IADNet achieves an mAP@50 of 80.3%, outperforming the baseline YOLO11m by 3.4 percentage points. On the self-constructed mining low-light dataset Lowlight_Mine, the proposed method achieves 92.3% Precision, 82.0% Recall, 89.3% mAP@50, and 57.8% mAP@50:95, showing favorable performance in object detection tasks under mining-related low-light scenarios. On the DARK FACE dataset, IADNet achieves 54.6% mAP@50 and 31.2% mAP@50:95, further indicating its robustness under real low-light conditions. On the synthetic low-light Dark_VOC dataset, IADNet attains an mAP@50 of 91.6%, and on the normal-light VOC dataset, it achieves an mAP@50 of 93.0%, suggesting that the proposed method maintains stable detection performance under the evaluated illumination conditions. These results indicate that IADNet improves low-light object detection performance and provides a useful experimental reference for object detection tasks in mining-related low-light scenarios. Full article

The objective of this study was to evaluate colour changes in cotton fibres within knitted fabric structures under different light exposure conditions and to assess the applicability of forensic analytical methods for this purpose. Fabrics of three distinct colours were exposed to two types of irradiation: natural sunlight and artificial light in a controlled climatic chamber. A multi-scale analytical approach was applied, including visual inspection and stereomicroscopy for macro-level evaluation, followed by bright-field microscopy, fluorescence microscopy, and UV–Vis microspectrophotometry for single-fibre characterisation. Visual assessment of fabrics revealed perceptible colour differences between exposed and unexposed samples, whereas stereomicroscopy did not consistently enhance the detection of these alterations. Bright-field and fluorescence microscopy showed no visually perceptible differences between fibres from exposed and unexposed fabrics of the same colour. Microspectrophotometric measurements did not reliably capture colour changes in single cotton fibres, particularly in samples exposed to natural sunlight. Furthermore, total colour difference (ΔE) values, ranging from 0.248 to 6.652, were found to be unreliable at the single-fibre level due to significant spatial variability across different measurement sites. The findings indicate that, while light exposure may induce perceptible colour alterations in cotton knitted fabrics, the forensic examination of single fibres does not necessarily reflect these macro-scale changes. From a forensic perspective, the stability of microscopic and microspectrophotometric characteristics supports reliable fibre comparison, even after post-event exposure to sunlight. Full article

Robotic systems are increasingly being deployed in mining operations to support tasks such as inspection, navigation, environmental monitoring, and safety supervision. However, mining environments present significant challenges for robotic perception due to dynamic terrain conditions, poor illumination, airborne dust, and frequent disturbances caused by excavation and heavy machinery. Conventional frame-based vision systems often struggle under these conditions due to motion blur, latency, and limited dynamic range. This study proposes a system-level conceptual framework for integrating event-based sensing into robotic mining systems in order to support perception in highly dynamic and safety-critical environments, with the aim of improving responsiveness and robustness under such conditions. Event-based cameras, inspired by biological vision, asynchronously detect brightness changes at the pixel level and provide microsecond temporal resolution with high dynamic range and low latency. The proposed framework combines event cameras with complementary sensing modalities including LiDAR, inertial measurement units, and RGB cameras to form a multi-sensor perception architecture. The framework is structured into multiple functional layers encompassing environmental sensing, event-driven perception, sensor fusion and AI processing, digital twin integration, and autonomous decision-making. Potential application scenarios including robotic tunnel inspection, autonomous navigation of mining robots, hazard detection, multi-agent cooperation in mining sites, and real-time digital twin updating are also discussed. The proposed framework provides a unified system-level reference architecture intended to guide future implementation and validation. Full article

The indoor microclimate of museums plays an essential role in preserving priceless cultural heritage for future generations and in ensuring visitors’ comfort and health. In this context, the present study aimed to evaluate indoor air quality, the degree of fungal contamination, and visitors’ perceptions in a museum environment through an integrated, interdependent approach. Measurements of the physicochemical parameters of air quality (temperature, relative humidity, CO₂, TVOC, HCHO, PM_2.5 and PM₁₀, negative and positive ions and brightness) were carried out in three exhibition halls within a museum in Oradea, Romania, during the period January–August 2024. Fungal contamination was assessed using surface and air samples, with classical isolation and microscopic identification methods. Visitors’ perceptions were analysed using a standardised questionnaire that focused on perceived comfort and visit duration. The results showed that the parameters defining indoor air quality generally fell within the limits set by the international standards in force, with occasional exceedances. These conditions are associated with the presence of fungi of the genera Cladosporium, Penicillium, and Aspergillus in the air and on museum exhibits, which pose risks to human health and the deterioration of the exhibited materials. The statistical decision-making model determined the critical thresholds above which visitor behaviour changed visibly. The results highlighted the importance of maintaining a stable microclimate in museum spaces, not only for the protection of exhibits, but also for optimising the cultural experience. Indoor air quality indicators and fungal microflora can only affect vulnerable people or those with pre-existing conditions. Occasional visitors do not present a significant risk of developing new conditions, considering the limited duration of exposure. Full article

Lighting quality in interior spaces is not determined solely by horizontal illuminance at the workplace, but to a large extent by the spatial distribution of light, in particular by the luminance of ceilings and walls. Building on classical principles of lighting technology and visual perception, this article examines the influence of the ratio of indirect to direct lighting on the perception of room brightness, the spatial impression, and overall preference. To this end, two complementary studies were conducted: a visual assessment of realistic room simulations and a user study in a real meeting room with variable illuminance levels and systematically varied proportions of indirect and direct lighting. The results consistently show that perceived room brightness and user preference correlate much more strongly with the illumination of ceilings and walls than with the horizontally measured illuminance on the table, which was kept constant. A balanced ratio of indirect to direct light—typically in the range of approximately 35% to 65% indirect lighting—is preferred by users, whereas predominantly direct or nearly purely indirect lighting is associated with lower acceptance. The study clearly demonstrates that existing standards, which primarily focus on horizontal illuminance, neglect essential aspects of lighting quality. The findings highlight the need to systematically integrate light distribution, vertical illuminance, and spatial–psychological effects into lighting design, evaluation, and standardization in order to achieve visually comfortable, widely accepted, and spatially appropriate lighting solutions. Full article

In contemporary museum design, architects increasingly treat spatial experience as a medium of visitor engagement, yet movement is often reduced to a problem of routing and orientation rather than recognised as engagement in its own right. This study shows how Steven Holl’s parallax operates as a motivational mechanism at the Kiasma Museum of Contemporary Art. Parallax, a phenomenological and ecological construct, is examined through oblique thresholds, overlapping perspectives, and layered illumination. Integrating phenomenology, ecological psychology, and spatial configuration analysis, this study links embodied perception to measurable spatial properties. Spatial relations were quantified using space syntax—axial line analysis, justified graphs, and isovist analysis—alongside luminance and visual saliency mapping of Kiasma’s second and third floors. The results reveal a dominant ring structure in which visibility tightens at thresholds and views shift continuously along the route. Pronounced brightness gradients accompany these transitions and intensify perceived change along the sequence. These coupled spatial and luminous strategies may encourage exploratory navigation, positioning wayfinding as integral to the museum experience. This study argues that parallax links spatial configuration to embodied engagement, emerging as a perceptual effect produced through the interaction of spatial layout, luminous modulation, and bodily movement rather than functioning as a fixed design principle. Full article

In complex traffic environments, image degradation caused by haze, low illumination, and occlusion significantly undermines the reliability of vehicle and pedestrian detection. To address these challenges, this paper proposes an aerial vision framework that tightly couples multi-level image enhancement with a lightweight detection architecture. At the image preprocessing stage, a cascaded “dehazing + enhancement” module is constructed, where a learning-based dehazing method is employed to restore long-range details affected by scattering artifacts. Additionally, structural fidelity is enhanced in low-light regions, while global brightness consistency is achieved. On the detection side, a lightweight yet robust detection architecture, termed GDEIM-SF, is designed. It adopts GoldYOLO as the lightweight backbone and integrates D-FINE as an anchor-free decoder. Moreover, two key modules, CAPR and ASF, are incorporated to enhance high-frequency edge modeling and multi-scale semantic alignment. Through evaluation on the VisDrone dataset, the proposed method achieves improvements of approximately 2.5 to 2.7 percentage points in core metrics such as mAP@50-90 compared to similar lightweight models, while maintaining a low parameter count and computational overhead. This ensures a balanced trade-off among detection accuracy, inference efficiency, and deployment adaptability, providing a practical and efficient solution for UAV-based visual perception tasks under challenging imaging conditions. Full article

Adverse weather conditions, such as fog, degrade image quality and affect the performance of deep learning-based image processing algorithms, whereas advanced driver assistance systems (ADASs) urgently demand image clarity and large-field-of-view perception in foggy environments. Existing image dehazing methods rarely consider the non-uniform and dense distribution of particles in fog, leading to severe attenuation of background information. Image stitching, owing to the low-brightness and low-texture characteristics of ADAS scenarios and differences between sensors, faces challenges such as difficult feature point extraction and matching and poor stitching quality. To address these issues, this study proposes a non-uniform dehazing method based on Deformable Convolution v4 (DCNv4), designing a DCNv4-based transform-like network to achieve long-range dependence and adaptive spatial aggregation, combined with a lightweight Retinex-inspired Transformer for color correction and structure refinement. Meanwhile, a multi-plane scale constraint module is introduced based on the LightGlue feature matching network to improve matching accuracy and homography matrix estimation precision, and an adaptive fusion stitching method is adopted to eliminate artifacts and transition zones. Experimental results show that the proposed method effectively improves feature matching accuracy and homography matrix calculation precision, achieving Peak Signal-to-Noise Ratios (PSNRs) of 22.78 dB and 24.34 dB on the NH-HAZE and BRAS datasets, respectively, which are superior to those of existing methods. This provides a reliable environmental perception solution for autonomous driving in foggy environments, verifying its effectiveness and practicality. Full article

To facilitate the display of solar images, captured solar images are often subjected to tone-mapping and enhancement. Accordingly, it is necessary to assess the quality of solar images before and after tone-mapping. However, there exist certain differences in human subjective perception under different ambient light intensities and when using different displays. Therefore, this paper proposes an image-quality assessment algorithm for solar tone-mapped images based on visual simulation. By effectively modeling the display characteristics model and the human visual system (HVS) model, the modeled images can reflect the perceptual effects of the human visual system under different ambient lighting conditions and display devices. Feeding modeled images into general image-quality assessment (IQA) metrics enables a better alignment with human visual perception. The proposed approach has been validated by two metrics: the solar IQA metric T based on the image power spectrum, and the IQA metric S based on the signal detection probability. We conducted subjective quality assessment experiments in a bright indoor environment with an ambient light intensity of 400 lux. By adjusting the display brightness, the Ambient Contrast Ratio (ACR) was controlled at 226.69 and 17.83, respectively. When the ACR was 226.69, the subjective Spearman Rank Correlation Coefficient (SRCC) of the T metric for the input before and after modeled increased by 1.83%, and that of the S metric by 5.44%. In addition, at an ACR of 17.83, the subjective SRCC of the metric T increased by 1.79%, while that of the metric S by 8.38%. We also conducted a regression test on the tone-mapping enhancement parameters using the metric S, and the test results demonstrated that the images generated from the metric with modeled image input yielded better visual effects. Full article

Real-time perception of bearing rotation angles is essential for structural health assessment of bridges. However, existing vision-based rotation angle measurement methods exhibit limited robustness to time-varying operational conditions and tracking errors, particularly in practical applications of bridge monitoring. To address this limitation, this study presents an advanced computer vision-based monitoring technology for bridge bearing rotation angles by incorporating specifically configured retroreflective targets, an efficient target tracking approach, and a rotation angle calculation algorithm. Firstly, under LED illumination, retroreflective targets appear as bright, high-contrast features in the images, facilitating precise detection and tracking. Secondly, target centroids are tracked with sub-pixel accuracy through thresholding, edge extraction, and ellipse fitting. Lastly, the bearing rotation angle is calculated by analyzing the angle between the two characteristic lines formed by the target centroids. To validate the effectiveness of the proposed method, comprehensive numerical investigations were conducted, and the results showed that the proposed method maintained high accuracy across various imaging conditions. Additionally, comparative analysis with an existing advanced method also revealed that the proposed method exhibits superior measurement performance even under target tracking uncertainties. To investigate its feasibility and validate its practical effectiveness, a field application on an 80 m + 80 m continuous beam was conducted, and minute rotation angle measurements during 23 railway train drive-by events were obtained using the proposed method, yielding a root mean square error of 0.0008° and mean absolute error of 0.0007°. The successful development and field deployment demonstrate significant potential for advancing structural health monitoring technologies, contributing to intelligent infrastructure management through automated monitoring and early warning capabilities. Full article

The lighting environment has transcended purely functional illumination and has evolved into a critical medium for orchestrating narrative rhythm and modulating audience emotional responses. However, existing studies often examine photometric properties and human emotional responses in isolation, failing to establish a quantitative coupling mechanism to elucidate the relationship between light distribution, visual attention, and emotional states. This study aims to quantify the coupling mechanisms between luminous environmental parameters (illuminance and CCT), visual attention distribution, and emotional states (PAD) in immersive narrative exhibition spaces for the optimization of visitor experience. Four screen-based simulated narrative scenes were constructed with different illumination levels (low/high) and four levels of correlated color temperature (2700 K, 3000 K, 4000 K, and 5000 K). Using the SIFT algorithm, the illuminance pseudo-color map and the eye-tracking heat map were spatially registered to quantify the spatial correlation between the physical light field and the visual attention field. The results demonstrate a significant nonlinear coupling effect: high-illuminance cold light (4000 K, 544 lx) establishes a strong guidance mechanism, with a high spatial correlation between visual attention and brightness ($r$ = 0.82), which significantly enhances physiological arousal and perceived dominance. Conversely, low-illuminance warm light (2700 K, 150 lx) leads to a weak coupling state ($r$ = 0.62), which promotes free visual exploration, thereby improving pleasure and perceived immersion. These results suggest that lighting design should not be treated as a fixed set of parameters, but rather as an adjustable strategy that responds to changes in visual attention and emotional experience. By modifying the strength of visual and optical interaction, lighting conditions can influence how visitors move from initial perception to emotional engagement. This provides practical support for applying evidence-based lighting strategies in the design of cultural heritage spaces. Full article