Search Results (936)

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article

As crucial transportation hubs and economic nodes, the underwater security and infrastructure maintenance of harbors are of paramount importance. Harbors are characterized by high vessel traffic and complex underwater environments, where traditional underwater inspection methods, such as diver operations, face challenges of low efficiency, high risk, and limited operational range. This paper introduces a collaborative survey and disposal system that integrates a deformable unmanned surface vehicle (USV) with a lightweight remotely operated vehicle (ROV). The USV is equipped with a side-scan sonar (SSS) and a multibeam echo sounder (MBES), enabling rapid, large-area searches and seabed topographic mapping. The ROV, equipped with an optical camera system, forward-looking sonar (FLS), and a manipulator, is tasked with conducting close-range, detailed observations to confirm and dispose of abnormal objects identified by the USV. Field trials were conducted at an island harbor in the South China Sea, where simulated underwater objects, including an iron drum, a plastic drum, and a rubber tire, were deployed. The results demonstrate that the USV-ROV collaborative system effectively meets the demands for underwater environmental measurement, object localization, identification, and disposal in complex harbor environments. The USV acquired high-resolution (0.5 m × 0.5 m) three-dimensional topographic data of the harbor, effectively revealing its topographical features. The SSS accurately localized and preliminarily identified all deployed simulated objects, revealing their acoustic characteristics. Repeated surveys revealed a maximum positioning deviation of 2.2 m. The lightweight ROV confirmed the status and location of the simulated objects using an optical camera and an underwater positioning system, with a maximum deviation of 3.2 m when compared to the SSS locations. The study highlights the limitations of using either vehicle alone. The USV survey could not precisely confirm the attributes of the objects, whereas a full-area search of 0.36 km² by the ROV alone would take approximately 20 h. In contrast, the USV-ROV collaborative model reduced the total time to detect all objects to 9 h, improving efficiency by 55%. This research offers an efficient, reliable, and economical practical solution for applications such as underwater security, topographic mapping, infrastructure inspection, and channel dredging in harbor environments. Full article

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track vehicles, enabling robust safety assessments. However, pedestrian detection remains challenging due to diverse motion patterns, varying clothing colors, occlusions, and positional differences. This study introduces an innovative approach that integrates multiple surveillance cameras at signalized intersections, regardless of their types or resolutions. Two distinct convolutional neural network (CNN)-based detection algorithms accurately track road users across multiple views. The resulting trajectories undergo analysis, smoothing, and integration, enabling detailed traffic scene reconstruction and precise identification of vehicle–pedestrian conflicts. The proposed framework achieved 97.73% detection precision and an average intersection over union (IoU) of 0.912 for pedestrians, compared to 68.36% and 0.743 with a single camera. For vehicles, it achieved 98.2% detection precision and an average IoU of 0.955, versus 58.78% and 0.516 with a single camera. These findings highlight significant improvements in detecting and analyzing traffic conflicts, enhancing the identification and mitigation of potential hazards. Full article

The identification of estrus at the right moment allows for a higher success of fecundity with artificial insemination. Evaluating changes in body surface temperature of sows during the estrus period using an infrared thermography camera (ITC) can provide an accurate model to predict these changes. This pilot study comprised nine crossbred Large White x Landrace sows, providing 59 data records for analysis. Observed changes in the behavior and physiological signs of the sows signaled the identification of estrus. Images of the ocular area, ear tips, breast, back, vulva, and perianal area were collected with the ITC. The images were analyzed using the FLIR Thermal Studio Starter software. Infrared mean temperatures were reported and compared using ANOVA and Tukey–Kramer tests (p < 0.05). Supervised machine learning models were tested using random forest (RF), Conditional inference trees (Ctree), Partial least squares (PLS), and K-nearest neighbors (KNN), and the method performance was measured using a confusion matrix. The orbital region showed significant differences between estrus and non-estrus states in sows. In the confusion matrix, the algorithm predicted estrus with 87% accuracy in the test set, which contained 40% of the data, when agitated behavior was combined with orbital area temperature. These findings suggest the potential for integrating behavioral and physiological observations with orbital thermography and machine learning to detect estrus in sows under field conditions accurately. Full article

The study presents a comparative analysis of five distinct image processing methodologies for roadside wild animal detection using thermal imagery, aiming to identify an optimal approach for embedded system implementation to mitigate wildlife–vehicle collisions. The evaluated techniques included the following: bilateral filtering followed by thresholding and SIFT feature matching; Gaussian filtering combined with Canny edge detection and contour analysis; color quantization via the nearest average algorithm followed by contour identification; motion detection based on absolute inter-frame differencing, object dilation, thresholding, and contour comparison; and animal detection based on a YOLOv8n neural network. These algorithms were applied to sequential thermal images captured by a custom roadside surveillance system incorporating a thermal camera and a Raspberry Pi processing unit. Performance evaluation utilized a dataset of consecutive frames, assessing average execution time, sensitivity, specificity, and accuracy. The results revealed performance trade-offs: the motion detection method achieved the highest sensitivity (92.31%) and overall accuracy (87.50%), critical for minimizing missed detections, despite exhibiting the near lowest specificity (66.67%) and a moderate execution time (0.126 s) compared to the fastest bilateral filter approach (0.093 s) and the high-specificity Canny edge method (90.00%). Consequently, considering the paramount importance of detection reliability (sensitivity and accuracy) in this application, the motion-based methodology was selected for further development and implementation within the target embedded system framework. Subsequent testing on diverse datasets validated its general robustness while highlighting potential performance variations depending on dataset characteristics, particularly the duration of animal presence within the monitored frame. Full article

This study presents a high-fidelity image acquisition method for asphalt film structure to address the challenge of capturing mesoscale structures, especially fine mineral filler and asphalt mastic. The method is particularly applied to the analysis of the mortar structure in reclaimed asphalt pavement (RAP) mixtures. A digital camera combined with image stacking and texture suppression techniques was used to develop a reproducible imaging protocol. The resulting sub-pixel images significantly improved clarity and structural integrity, particularly for particles smaller than 0.075 mm. U-Net-based segmentation identified 588,513 aggregate particles—34 times more than in standard images (17,428). Among them, 95% were smaller than 0.075 mm compared to just 45% in standard images. Furthermore, segmentation accuracy reached 99.3% in high-resolution images, surpassing the 98.1% in standard images. These results confirm the method’s strong capability to preserve microscale features and enhance fine particle recognition, making it more effective than conventional imaging approaches. This study bridges physical and digital workflows in asphalt material analysis, offering a scalable, reproducible pipeline for fine-structure identification. The methodology provides foundational support for data-driven pavement modeling, material optimization, and future integration into digital twin frameworks for intelligent infrastructure systems. Full article

Against the global backdrop of energy transition, the precise assessment of urban rooftop photovoltaic (PV) system capacity is recognized as crucial for optimizing the energy structure and enhancing the sustainable utilization efficiency of spatial resources. Publicly available aerial imagery is characterized by non-orthorectified issues; direct utilization is known to lead to geometric distortions in rooftop PV and errors in capacity prediction. To address this, a dual-optimization framework is proposed in this study, integrating monocular vision-based 3D reconstruction with a lightweight linear model. Leveraging the orthogonal characteristics of building structures, camera self-calibration and 3D reconstruction are achieved through geometric constraints imposed by vanishing points. Scale distortion is suppressed via the incorporation of a multi-dimensional geometric constraint error control strategy. Concurrently, a linear capacity-area model is constructed, thereby simplifying the complexity inherent in traditional multi-parameter fitting. Utilizing drone oblique photography and Google Earth public imagery, 3D reconstruction was performed for 20 PV-equipped buildings in Wuhan City. Two buildings possessing high-precision field survey data were selected as typical experimental subjects for validation. The results demonstrate that the 3D reconstruction method reduced the mean absolute percentage error (MAPE)—used here as an estimator of measurement uncertainty—of PV area identification from 10.58% (achieved by the 2D method) to 3.47%, while the coefficient of determination (R²) for the capacity model reached 0.9548. These results suggest that this methodology can provide effective technical support for low-cost, high-precision urban rooftop PV resource surveys. It has the potential to significantly enhance the reliability of energy planning data, thereby contributing to the efficient development of urban spatial resources and the achievement of sustainable energy transition goals. Full article

Infrared cameras play an important role in various fields, such as research and development, and inspection and surveillance; the higher the performance of an infrared camera, the more important the specifications are. However, in the field of long-range surveillance, it is difficult to strictly grasp the performance of infrared cameras because they are affected by atmospheric conditions. The performance of DRI (Detection, Recognition, Identification), one of the specifications used in infrared cameras for surveillance, is merely a simulated value from each company. In this verification, we used a MWIR long focal infrared camera to verify whether there was any difference between the simulation values in a real environment and the actual usage conditions. Full article

Hyperspectral imaging, in combination with microscopy, can increase material discrimination compared to standard microscopy. We explored the potential of discriminating pellet microplastic materials using a hyperspectral short-wavelength infrared (SWIR) camera, providing 100 bands in the 1100–1650 nm range, in combination with reflection microscopy. The identification of the most relevant spectral bands helps to increase system cost efficiency. The use of fewer bands reduces memory and processing requirements, and can also steer the development of sustainable, cost-efficient sensors with fewer bands. For this purpose, we present a genetic algorithm to perform band relevance analysis and propose novel algorithm optimizations. The results show that a few spectral bands (between 6 and 9) are sufficient for accurate (>80%) pixel discrimination of all 22 types of microplastic waste, contributing to sustainable development goals (SDGs) such as SDG 6 (‘clean water and sanitation’) or SDG 9 (‘industry, innovation, and infrastructure’). In addition, we study the impact of the classifier method and the width of the spectral response on band selection, neither of which has been addressed in the current state-of-the-art. Finally, we propose a method to steer band selection towards a more balanced distribution of classification accuracy, increasing its applicability in multiclass applications. Full article

In real-world dairy farming environments, object recognition models often suffer from missed or false detections due to complex backgrounds and cow occlusions. In response to these issues, this paper proposes FSCA-YOLO, a multi-object cow behavior recognition model based on an improved YOLOv11 framework. First, the FEM-SCAM module is introduced along with the CoordAtt mechanism to enable the model to better focus on effective behavioral features of cows while suppressing irrelevant background information. Second, a small object detection head is added to enhance the model’s ability to recognize cow behaviors occurring at the distant regions of the camera’s field of view. Finally, the original loss function is replaced with the SIoU loss function to improve recognition accuracy and accelerate model convergence. Experimental results show that compared with mainstream object detection models, the improved YOLOv11 in this section demonstrates superior performance in terms of precision, recall, and mean average precision (mAP), achieving 95.7% precision, 92.1% recall, and 94.5% mAP—an improvement of 1.6%, 1.8%, and 2.1%, respectively, over the baseline YOLOv11 model. FSCA-YOLO can accurately extract cow features in real farming environments, providing a reliable vision-based solution for cow behavior recognition. To support specific behavior recognition and in-region counting needs in multi-object cow behavior recognition and tracking systems, OpenCV is integrated with the recognition model, enabling users to meet the diverse behavior identification requirements in groups of cows and improving the model’s adaptability and practical utility. Full article

The identification of Aspergillus flavus (A. flavus), a fungus known for producing aflatoxins, poses a taxonomic challenge due to its morphological plasticity and similarity to closely related species. This article proposes a computational approach for its characterization across four culture media, using ultraviolet (UV) radiation imaging and radiomic analysis. Images were acquired with a camera controlled by a Raspberry Pi and processed to extract 408 radiomic features (102 per color channel and grayscale). Shapiro–Wilk and Levene’s tests were applied to verify normality and homogeneity of variances as prerequisites for an analysis of variance (ANOVA). Nine features showed statistically significant differences and, together with the culture medium type as a categorical variable, were used in a supervised classification stage with cross-validation. Classification using Support Vector Machines (SVM) achieved 97% accuracy on the test set. The results showed that the morphology of A. flavus varies significantly depending on the medium under UV radiation, with malt extract agar being the most discriminative. This non-invasive and low-cost approach demonstrates the potential of radiomics combined with machine learning to capture morphological patterns useful in the differentiation of fungi with optical response under UV radiation. Full article

Person re-identification aims to match images of the same individual across non-overlapping cameras by analyzing personal characteristics. Recently, Transformer-based models have demonstrated excellent capabilities and achieved breakthrough progress in this task. However, their high computational costs and inadequate capacity to capture fine-grained local features impose significant constraints on re-identification performance. To address these challenges, this paper proposes a novel Toward Efficient Transformer-based Person Re-identification (TE-TransReID) framework. Specifically, the proposed framework retains only the former L-th layer layers of a pretrained Vision Transformer (ViT) for global feature extraction while combining local features extracted from a pretrained CNN, thus achieving the trade-off between high accuracy and lightweight networks. Additionally, we propose a dual efficient feature-fusion strategy to integrate global and local features for accurate person re-identification. The Efficient Token-based Feature-Fusion Module (ETFFM) employs the gate-based network to learn fused token-wise features, while the Efficient Patch-based Feature-Fusion Module (EPFFM) utilizes a lightweight Transformer to aggregate patch-level features. Finally, TE-TransReID achieves a rank-1 of 94.8%, 88.3%, and 85.7% on Market1501, DukeMTMC, and MSMT17 with a parameter of 27.5 M, respectively. Compared to existing CNN–Transformer hybrid models, TE-TransReID maintains comparable recognition accuracy while drastically reducing model parameters, establishing an optimal equilibrium between recognition accuracy and computational efficiency. Full article

Polarization information is essential for material identification, stress mapping, biological imaging, and robust vision under strong illumination, yet conventional approaches rely on external polarization optics and active biasing, which are bulky, alignment-sensitive, and power-hungry. A more desirable route is to encode polarization at the pixel level and read it out at zero bias, enabling compact, low-noise, and polarization imaging. Low-symmetry layered semiconductors provide persistent in-plane anisotropy as a materials basis for polarization selectivity. Here, we construct an eight-terminal radial ‘star-shaped’ Au/ReS₂ metal-semiconductor junction array pixel that operates in a genuine photovoltaic mode under zero external bias based on the photothermoelectric effect. Based on this, electrical summation of phase-matched multi-junction channels increases the signal amplitude approximately linearly without sacrificing the two-lobed modulation depth, achieving ‘gain by stacking’ without external amplification. The device exhibits millisecond-scale transient response and robust cycling stability and, as a minimal pixel unit, realizes polarization-resolved imaging and pattern recognition. Treating linear combinations of channels as operators in the polarization domain, these results provide a general pixel-level foundation for compact, zero-bias, and scalable polarization cameras and on-pixel computational sensing. Full article

Unmanned aerial vehicles (UAVs) have become indispensable tools for surveillance, enabled by their ability to capture multi-perspective imagery in dynamic environments. Among critical UAV-based tasks, cross-platform person search—detecting and identifying individuals across distributed camera networks—presents unique challenges. Severe viewpoint variations, occlusions, and cluttered backgrounds in UAV-captured data degrade the performance of conventional discriminative models, which struggle to maintain robustness under such geometric and semantic disparities. To address this, we propose view-invariant person search (VIPS), a novel two-stage framework combining Faster R-CNN with a view-invariant re-Identification (VIReID) module. Unlike conventional discriminative models, VIPS leverages the semantic flexibility of large vision–language models (VLMs) and adopts a two-stage training strategy to decouple and align text-based ID descriptors and visual features, enabling robust cross-view matching through shared semantic embeddings. To mitigate noise from occlusions and cluttered UAV-captured backgrounds, we introduce a learnable mask generator for feature purification. Furthermore, drawing from vision–language models, we design view prompts to explicitly encode perspective shifts into feature representations, enhancing adaptability to UAV-induced viewpoint changes. Extensive experiments on benchmark datasets demonstrate state-of-the-art performance, with ablation studies validating the efficacy of each component. Beyond technical advancements, this work highlights the potential of VLM-derived semantic alignment for UAV applications, offering insights for future research in real-time UAV-based surveillance systems. Full article

Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration. Full article