Search Results (1,602)

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming. Full article

In order to enhance the application of thermoregulated materials, magnetic phase change microcapsules were prepared using a self-assembly method. Paraffin wax was chosen for its fine thermoregulation properties as the core material, while Fe₃O₄ nanoparticles doped in calcium carbonate served as the hybrid shell material. The microcapsules were then blended with sodium alginate and processed into seaweed fibers through wet spinning. The microstructure, thermal, and magnetic properties of the microcapsules were analyzed using scanning electron microscopy, energy dispersive X-ray spectroscopy, a laser particle size analyzer, an X-ray diffractometer, a differential scanning calorimeter, a thermogravimetric analyzer, and a vibrating sample magnetometer. The thermoregulation of the fibers was evaluated using a thermal infrared imager. The results indicated that the microcapsules had a uniform size distribution and good thermal properties. When the mass fraction of Fe₃O₄ nanoparticles was 8%, the microcapsules exhibited a saturation magnetization of 2.44 emu/g and an enthalpy value of 94.25 J/g, indicating effective phase change and magnetic properties. Furthermore, the thermoregulated seaweed fibers showed a high enthalpy value of 19.8 J/g with fine shape, offering potential for developing multifunctional fiber products. Full article

Infrared small target detection is a key technology with a wide range of applications, and the complex background and low signal-to-noise ratio characteristics of infrared images can greatly increase the difficulty and error rate of small target detection. In this paper, an uncertainty measurement method based on local component consistency is proposed to suppress the complex background and highlight the detection target. The method analyzes the local signal consistency of the image. It then constructs a confidence assignment function and uses the mutation entropy operator to measure local uncertainty. Then, the target energy information is introduced through an energy-weighting function to further enhance the signal. Finally, the target is extracted using an adaptive threshold segmentation algorithm. The experimental results show that the algorithm can effectively detect small infrared targets in complex backgrounds. And, the algorithm is at the leading edge in terms of performance; the processing frame rate can reach 3051 FPS (frame per second), 96 FPS, and 54 FPS for image data with a resolution of 256 × 256, 1920 × 1080, and 2560 × 1440, respectively. Full article

Regarding photocatalytic oxidative desulphurization (PODS), titanium oxide (TiO₂) is a promising contender as a catalyst due to its photocatalytic prowess and long-term performance in desulphurization applications. This work demonstrates the effectiveness of double-doping TiO₂ in silver (Ag) and molybdenum (Mo) for use as a novel catalyst in the desulphurization of light-cut hydrocarbons. FESEM, EDS, and AFM were used to characterize the morphology, doping concentration, surface features, grain size, and grain surface area of the Ag/Mo powder. On the other hand, XRD, FTIR spectroscopy, UV-Vis, and PL were used for structure and functional group detection and light absorption analysis based on TiO₂’s illumination properties. The microscopic images revealed nanoparticles with irregular shapes, and a 3D-AFM image was used to determine the catalyst’s physiognomies: 0.612 nm roughness and a surface area of 811.79 m²/g. The average sizes of the grains and particles were calculated to be 32.15 and 344.4 nm, respectively. The XRD analysis revealed an anatase structure for the doped TiO₂, and the FTIR analysis exposed localized functional groups, while the absorption spectra of the catalyst, obtained via UV-Vis, revealed a broad spectrum, including visible and near-infrared regions up to 1053.34 nm. The PL analysis showed luminescence with a lower emission intensity, indicating that the charge carriers were not thoroughly combined. This study’s findings indicate a desulphurization efficiency of 97%. Additionally, the promise of a nano-homogeneous particle distribution bodes well for catalytic reactions. The catalyst retains its efficiency when it is dried and reused, demonstrating its sustainable use while maintaining the desulphurization efficacy. This study highlights the potential of the double doping approach in enhancing the catalytic properties of TiO₂, opening up new possibilities for improving the performance of photo-oxidative processes. Full article

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones’ industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area’s temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research. Full article

Wildfires cause severe consequences, including property loss, threats to human life, damage to natural resources, biodiversity, and economic impacts. Consequently, numerous wildland fire detection systems were developed over the years to identify fires at an early stage and prevent their damage to both the environment and human lives. Recently, deep learning methods were employed for recognizing wildfires, showing interesting results. However, numerous challenges are still present, including background complexity and small wildfire and smoke areas. To address these challenging limitations, two deep learning models, namely CT-Fire and DC-Fire, were adopted to recognize wildfires using both visible and infrared aerial images. Infrared images detect temperature gradients, showing areas of high heat and indicating active flames. RGB images provide the visual context to identify smoke and forest fires. Using both visible and infrared images provides a diversified data for learning deep learning models. The diverse characteristics of wildfires and smoke enable these models to learn a complete visual representation of wildland fires and smoke scenarios. Testing results showed that CT-Fire and DC-Fire achieved higher performance compared to baseline wildfire recognition methods using a large dataset, which includes RGB and infrared aerial images. CT-Fire and DC-Fire also showed the reliability of deep learning models in identifying and recognizing patterns and features related to wildland smoke and fires and surpassing challenges, including background complexity, which can include vegetation, weather conditions, and diverse terrain, detecting small wildfire areas, and wildland fires and smoke variety in terms of size, intensity, and shape. CT-Fire and DC-Fire also reached faster processing speeds, enabling their use for early detection of smoke and forest fires in both night and day conditions. Full article

In peanut cultivation, the fact that the fruits develop underground presents significant challenges for mechanized harvesting, leading to high loss rates, with values that can exceed 30% of the total production. Since the harvest is conducted indirectly in two stages, losses are higher during the digging/inverter stage than the collection stage. During the digging process, losses account for about 60% to 70% of total losses, and this operation directly influences the losses during the collection stage. Experimental studies in production fields indicate a strong correlation between losses and the height of the windrow formed after the digging/inversion process, with a positive correlation coefficient of 98.4%. In response to this high correlation, this article presents a system for estimating the windrow profile during mechanized peanut harvesting, allowing for the measurement of crucial characteristics such as the height, width and shape of the windrow, among others. The device uses an infrared laser beam projected onto the ground. The laser projection is captured by a camera strategically positioned above the analyzed area, and through advanced image processing techniques using triangulation, it is possible to measure the windrow profile at sampled points during a real experiment under direct sunlight. The technical literature does not mention any system with these specific characteristics utilizing the techniques described in this article. A comparison between the results obtained with the proposed system and those obtained with a manual profilometer showed a root mean square error of only 28 mm. The proposed system demonstrates significantly greater precision and operates without direct contact with the soil, making it suitable for dynamic implementation in a control mesh for a digging/inversion device in mechanized peanut harvesting and, with minimal adaptations, in other crops, such as beans and potatoes. Full article

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing. Full article

Infrared image denoising is a critical task in various applications, yet existing methods often struggle with preserving fine details and managing complex noise patterns, particularly under high noise levels. To address these limitations, this paper proposes a novel denoising method based on the Swin Transformer architecture, named SwinDenoising. This method leverages the powerful feature extraction capabilities of Swin Transformers to capture both local and global image features, thereby enhancing the denoising process. The proposed SwinDenoising method was tested on the FLIR and KAIST infrared image datasets, where it demonstrated superior performance compared to state-of-the-art methods. Specifically, SwinDenoising achieved a PSNR improvement of up to 2.5 dB and an SSIM increase of 0.04 under high levels of Gaussian noise (50 dB), and a PSNR increase of 2.0 dB with an SSIM improvement of 0.03 under Poisson noise ($\lambda$ = 100). These results highlight the method’s effectiveness in maintaining image quality while significantly reducing noise, making it a robust solution for infrared image denoising. Full article

We use non-destructive Digital Holographic Speckle Pattern Interferometry (DHSPI), post-processing image analysis and one-dimensional exponential analysis to visualize, map and describe the structural condition of a plaster-based material. The body is heated by infrared radiation for two different time windows and the cooling process that follows is monitored in time by the so-called interferograms that are developed and are the result of the superposition of the holographic recordings of the sample prior to the thermal load and at variable time intervals during the cooling process. The fringe patterns in the interferometric images reveal features and characteristics of the interior of the material, with the experimental method and the post-process analysis adopted in this work offering accuracy, sensitivity and full-field diagnosis, in a completely non-destructive manner, without the need of sampling. Full article

Based on the principle of self-imaging, a 1 × 2 graphene waveguide beam splitter is proposed in this work, which can split the graphene surface plasmons excited by far-infrared light. The multimode interference process in the graphene waveguide is analyzed by guided-mode propagation analysis (MPA), and then the imaging position is calculated. The simulation results show that the incident beam can be obviously divided into two parts by the self-imaging of the graphene surface plasmon. In addition, the influences of the excited light wavelength, Fermi level, dielectric environment on the transmission efficiency are studied, which provide a reference for the research of graphene waveguide related devices. Full article

Carbon nanotube field-effect transistors (CNT-FETs) have shown great promise in infrared image detection due to their high mobility, low cost, and compatibility with silicon-based technologies. This paper presents the design and simulation of a column-level analog front-end (AFE) circuit tailored for carbon-based short-wave infrared (SWIR) photodetectors. The AFE integrates a Capacitor Trans-impedance Amplifier (CTIA) for current-to-voltage conversion, coupled with Correlated Double Sampling (CDS) for noise reduction and operational amplifier offset suppression. A 10-bit/125 kHz Successive Approximation analog-to-digital converter (SAR ADC) completes the signal processing chain, achieving rail-to-rail input/output with minimized component count. Fabricated using 0.18 μm CMOS technology, the AFE demonstrates a high signal-to-noise ratio (SNR) of 59.27 dB and an Effective Number of Bits (ENOB) of 9.35, with a detectable current range from 500 pA to 100.5 nA and a total power consumption of 7.5 mW. These results confirm the suitability of the proposed AFE for high-precision, low-power SWIR detection systems, with potential applications in medical imaging, night vision, and autonomous driving systems. Full article

The introduction of optimized nanoheaters, which function as theranostic agents integrating both diagnostic and therapeutic processes, holds significant promise in the medical field. Therefore, developing strategies for selecting and utilizing optimized plasmonic nanoheaters is crucial for the effective use of nanostructured biomedical agents. This work elucidates the use of the Joule number ($J_o$) as a figure of merit to identify high-performance plasmonic theranostic agents. A framework for optimizing metallic nanoparticles for heat generation was established, uncovering the size dependence of plasmonic nanoparticles optical heating. Gold nanospheres (AuNSs) with a diameter of 50 nm and gold nanorods (AuNRs) with dimensions of $41\times 10$ nm were identified as effective nanoheaters for visible (530 nm) and infrared (808 nm) excitation. Notably, AuNRs achieve higher $J_o$ values than AuNSs, even when accounting for the possible orientations of the nanorods. Theoretical results estimate that $41\times 10$ nm gold nanorods have an average Joule number of 80, which is significantly higher compared to larger rods. The photothermal performance of optimal and suboptimal nanostructures was evaluated using photoacoustic imaging and photothermal therapy procedures. The photoacoustic images indicate that, despite having larger absorption cross-sections, the large nanoparticle volume of bigger particles leads to less efficient conversion of light into heat, which suggests that the use of optimized nanoparticles promotes higher contrast, benefiting photoacoustic-based procedures in diagnostic applications. The photothermal therapy procedure was performed on S180-bearing mice inoculated with $41\times 10$ nm and $90\times 25$ nm PEGylated AuNRs. Five minutes of laser irradiation of tumor tissue with $41\times 10$ nm produced an approximately 9.5% greater temperature rise than using $90\times 25$ AuNRs in the therapy trials. Optimizing metallic nanoparticles for heat generation may reduce the concentration of the nanoheaters used or decrease the light fluence for bioscience applications, paving the way for the development of more economical theranostic agents. Full article

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives. Full article

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes. Full article