Search Results (327)

Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (XGBoost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the XGBoost model’s performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. The proposed temperature detection method offers higher accuracy and versatility, contributing to more efficient detection of abnormal ship temperatures at night. Full article

The long-lasting outbreak of the pine shoot beetle (PSB, Tomicus spp.) threatens forest ecological security. Effective monitoring is urgently needed for the Integrated Pest Management (IPM) of this pest. UAV-based hyperspectral remote sensing (HRS) offers opportunities for the early and accurate detection of PSB attacks. However, the insufficient exploration of spectral and structural information from early-attacked crowns and the lack of suitable detection models limit UAV applications. This study developed a UAV-based framework for detecting early-stage PSB attacks by integrating hyperspectral images (HSIs), LiDAR point clouds, and structure from motion (SfM) photogrammetry data. Individual tree segmentation algorithms were utilized to extract both spectral and structural variables of damaged tree crowns. Random forest (RF) was employed to determine the optimal detection model as well as to clarify the contributions of the candidate variables. The results are as follows: (1) Point cloud segmentation using the Canopy Height Model (CHM) yielded the highest crown segmentation accuracy (F-score: 87.80%). (2) Near-infrared reflectance exhibited the greatest decrease for early-attacked crowns, while the structural variable intensity percentile (int_P50-int_P95) showed significant differences (p < 0.05). (3) In the RF model, spectral variables were predominant, with LiDAR structural variables serving as a supplement. The anthocyanin reflectance index and int_kurtosis were identified as the best indicators for early detection. (4) Combining HSI with LiDAR data obtained the best RF model accuracy (classification accuracy: 87.31%; Kappa: 0.8275; SDR estimation accuracy: R² = 0.8485; RMSE_cv = 3.728%). RF integrating HSI and SfM data exhibited similar performance. In conclusion, this study identified optimal spectral and structural variables for UAV monitoring and improved HRS model accuracy and thereby provided technical support for the IPM of PSB outbreaks. Full article

Aiming at the inherent nature and complexity of the influence of in-orbit micro-vibration in the imaging quality of segmented telescopes, a dynamic full-link opto-mechanical integration analysis method is proposed. The method is based on the measured micro-vibration signals of the infrared refrigerator, using the finite element method to perform the transient response analysis of the opto-mechanical system in Patran/Nastran software. The interface tool is written by Matlab to achieve the calculation of rigid body displacement and real-time data interaction with Zemax. The results show that when the working wavelength is 1 μm, the optical system has a wavefront error Root-Mean-Square value of less than 0.071λ in 4 s. Evaluating the effect of micro-vibration on the imaging quality of the system in terms of the peak ratio of the point spread function. When the exposure time was 2 s, the ratio maximum values of 0.4628 and 0.6207 were reached for the X-axis and Y-axis, respectively. The method provides an important reference basis for the evaluation of imaging quality of an optical system under micro-vibration environment with a long exposure time. Full article

Existing aerial forest fire monitoring data primarily consist of infrared or visible light images. However, there is a lack of in-depth research on the ability of models to perceive fire regions across different spectral images. To address this, we first constructed a dataset of infrared and visible light images captured in the same scene, from the same perspective, and at the same time, with pixel-level segmentation annotations of the flame regions in the images. In response to the issues of poor flame segmentation performance in the current fire images and the large number of learnable parameters in large models, we propose an improved large model algorithm, SAMFA (Segmentation Anything Model, Fire, Adapter). Firstly, while freezing the original parameters of the large model, only the additionally incorporated Adapter module is fine-tuned to better adapt the network to the specificities of the flame segmentation task. Secondly, to enhance the network’s perception of flame edges, a U-shaped mask decoder is designed. Lastly, to reduce the training difficulty, a progressive strategy combining self-supervised and fully supervised learning is employed to optimize the entire model. We compared SAMFA with five state-of-the-art image segmentation algorithms on a labeled public dataset, and the experimental results demonstrate that SAMFA performs the best. Compared to SAM, SAMFA improves the IoU by 11.94% and 6.45% on infrared and visible light images, respectively, while reducing the number of learnable parameters to 11.58 M. Full article

Background: Indocyanine green (ICG) fluorescence has seen extensive application across medical and surgical fields, praised for its real-time navigation capabilities and low toxicity. Initially employed to assess liver function, ICG fluorescence is now integral to liver surgery, aiding in tumor detection, liver segmentation, and the visualization of bile leaks. This study reviews current protocols and ICG fluorescence applications in liver surgery, with a focus on optimizing timing and dosage based on clinical indications. Methods: Following PRISMA guidelines, we systematically reviewed the literature up to 27 January 2024, using PubMed and Medline to identify studies on ICG fluorescence used in liver surgery. A systematic review was performed to evaluate dosage and timing protocols for ICG administration. Results: Of 1093 initial articles, 140 studies, covering a total of 3739 patients, were included. The studies primarily addressed tumor detection (40%), liver segmentation (34.6%), and both (21.4%). The most common ICG fluorescence dose for tumor detection was 0.5 mg/kg, with administration occurring from days to weeks pre-surgery. Various near-infrared (NIR) camera systems were utilized, with the PINPOINT system most frequently cited. Tumor detection rates averaged 87.4%, with a 10.5% false-positive rate. Additional applications include the detection of bile leaks, lymph nodes, and vascular and biliary structures. Conclusions: ICG fluorescence imaging has emerged as a valuable tool in liver surgery, enhancing real-time navigation and improving clinical outcomes. Standardizing protocols could further enhance ICG fluorescence efficacy and reliability, benefitting patient care in hepatic surgeries. Full article

Infrared small target detection (IRSTD) aims to segment small targets from an infrared clutter background. However, the long imaging distance, complex background, and extremely limited number of target pixels pose great challenges for IRSTD. In this paper, we propose a new IRSTD method based on the deep graph neural network to fully extract and fuse the texture and structural information of images. Firstly, a clustering algorithm is designed to divide the image into several subgraphs as a prior knowledge to guide the initialization of the graph structure of the infrared image, and the image texture features are integrated to graph construction. Then, a graph feature extraction module is designed, which guides nodes to interact with features within their subgraph via the adjacency matrix. Finally, a hierarchical graph texture feature fusion module is designed to concatenate and stack the structure and texture information at different levels to realize IRSTD. Extensive experiments have been conducted, and the experimental results demonstrate that the proposed method has high interaction over union (IoU) and probability of detection (P_d) on public datasets and the self-constructed dataset, indicating that it has fine shape segmentation and accurate positioning for infrared small targets. Full article

This study addresses the challenge of monitoring oxide layer formation in 1045 steel, a critical issue affecting mechanical properties and phase stability during high-temperature processes (900 °C). To tackle this, an image processing algorithm was developed to detect and segment regions of interest (ROIs) in oxidized steel surfaces, utilizing infrared thermography as a non-contact, real-time measurement technique. Controlled heating experiments ensured standardized data acquisition, and the algorithm demonstrated exceptional accuracy with performance metrics such as 96% accuracy and a Dice coefficient of 96.15%. These results underscore the algorithm’s capability to monitor oxide scale formation, directly impacting surface quality, thermal uniformity, and material integrity. The integration of thermography with machine learning techniques enhances steel manufacturing processes by enabling precise interventions, reducing material losses, and improving product quality. This work highlights the potential of advanced monitoring systems to address challenges in industrial steel production and contribute to the sustainability of advanced steel materials. Full article

The aquatic plant species Eichhornia crassipes, commonly known as water hyacinth, is indigenous to South America and is considered an invasive species. The invasive water hyacinth has caused significant economic and ecological damage by preventing sunlight from penetrating the surface of the water, resulting in the loss of aquatic life. To quantify the invasiveness and address the issue of accurately identifying plant species, water hyacinths have prompted numerous researchers to propose approaches to detect regions occupied by water hyacinths. One such solution involves the utilization of multispectral imaging which obtain detailed information about plant species based on the surface reflectance index. This is achieved by analyzing the intensity of light spectra at different wavelengths emitted by each plant. However, the use of multispectral imagery presents a potential challenge since there are various spectral indices that can be used to capture different information. Despite the high accuracy of these multispectral images, there remains a possibility that plants similar to water hyacinths may be misclassified if the right spectral index is not chosen. Considering this challenge, the objective of this research is to develop a low-cost multispectral camera capable of capturing multispectral images. The camera will be equipped with two infrared light spectrum filters with wavelengths of 720 and 850 nanometers, respectively, as well as red, blue, and green light spectrum filters. Additionally, the implementation of the U-Net architecture is proposed for semantic segmentation to accurately identify water hyacinths, as well as other classes such as lakes and land. An accuracy rate of 96% was obtained for the identification of water hyacinths using data captured by an autonomous drone constructed in the laboratory flying at an altitude of 10 m. We also analyzed the contribution each of the infrared layers to the camera’s spectrum setup. Full article

Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the diversity and complexity of maize straw coverage in fields after harvest. To improve the precision and effectiveness of remote sensing recognition for maize straw types, a novel method was proposed. This method utilized unmanned aerial vehicle (UAV) multispectral imagery, integrated the Stacking Enhanced Straw Index (SESI) introduced in this study, and combined height, shape, and spectral characteristics to improve recognition accuracy. Using the original five-band multispectral imagery, a new nine-band image of the study area was constructed by integrating the calculated SESI, Canopy Height Model (CHM), Product Near-Infrared Straw Index (PNISI), and Normalized Difference Vegetation Index (NDVI) through band combination. An object-oriented classification method, utilizing a “two-step segmentation with multiple algorithms” strategy, was employed to integrate height, shape, and spectral features, enabling rapid and accurate mapping of maize straw types. The results showed that height information obtained from the CHM and spectral information derived from SESI were essential for accurately classifying maize straw types. Compared to traditional methods that relied solely on spectral information for recognition of maize straw types, the proposed approach achieved a significant improvement in overall classification accuracy, increasing it by 8.95% to reach 95.46%, with a kappa coefficient of 0.94. The remote sensing recognition methods and findings for maize straw types presented in this study can offer valuable information and technical support to agricultural departments, environmental protection agencies, and related enterprises. Full article

Coal, as a vital global energy resource, directly impacts the efficiency of power generation and environmental protection. Thus, rapid and accurate coal quality analysis is essential to promote its clean and efficient utilization. However, combined near-infrared spectroscopy and X-ray fluorescence (NIRS-XRF) spectroscopy often suffer from the particle size effect of coal samples, resulting in unstable and inaccurate analytical outcomes. This study introduces a novel correction method combining the Segment Anything Model (SAM) for precise particle segmentation and Data-Efficient Image Transformers (DeiTs) to analyze the relationship between particle size and ash measurement errors. Microscopic images of coal samples are processed with SAM to generate binary mask images reflecting particle size characteristics. These masks are analyzed using the DeiT model with transfer learning, building an effective correction model. Experiments show a 22% reduction in standard deviation (SD) and root mean square error (RMSE), significantly enhancing ash prediction accuracy and consistency. This approach integrates cutting-edge image processing and deep learning, effectively reducing submillimeter particle size effects, improving model adaptability, and enhancing measurement reliability. It also holds potential for broader applications in analyzing complex samples, advancing automation and efficiency in online analytical systems, and driving innovation across industries. Full article

The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production. Full article

In the background of high frequency and intensity forest fires driven by future warming and a drying climate, early detection and effective control of fires are extremely important to reduce losses. Meteorological satellite imagery is commonly used for near-real-time forest fire monitoring, thanks to its high temporal resolution. To address the misjudgments and omissions caused by solely relying on changes in infrared band brightness values and a single image in forest fire early discrimination, this paper constructs the angle slope indexes ANIR, AMIR, AMNIR, ∆ANIR, and ∆AMIR based on the reflectance of the red band and near-infrared band, the brightness temperature of the mid-infrared and far-infrared band, the difference between the AMIR and ANIR, and the index difference between time-series images. These indexes integrate the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. We also used the decomposed three-dimensional OTSU (maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data to address the different discrimination thresholds caused by different time and space backgrounds. In this paper, the Himawari-8 satellite imagery was used to detect forest fires based on the angle slope indices thresholds algorithm (ASITR), and the fusion of the decomposed three-dimensional OTSU and ASITR algorithm (FDOA). Results show that, compared with ASITR, the accuracy of FDOA decreased by 3.41% (0.88 vs. 0.85), the omission error decreased by 52.94% (0.17 vs. 0.08), and the overall evaluation increased by 3.53% (0.85 vs. 0.88). The ASITR has higher accuracy, and the fusion of decomposed three-dimensional OTSU and angle slope indexes can reduce forest fire omission error and improve the overall evaluation. Full article

Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance. Here, we present MonoSeg, a novel instance segmentation framework optimized for UAV perspective infrared vehicle detection. Our approach introduces three key innovations: (1) the Ghost Feature Bottle Cross module (GFBC), which enhances backbone feature extraction efficiency while significantly reducing computational over-head; (2) the Scale Feature Recombination module (SFR), which optimizes feature selection in the Neck stage through adaptive multi-scale fusion; and (3) Comprehensive Loss function that enforces precise instance boundary delineation. Extensive experimental evaluation on bench-mark datasets demonstrates that MonoSeg achieves state-of-the-art performance across standard metrics, including Box mAP and Mask mAP, while maintaining substantially lower computational requirements compared to existing methods. Full article

Multimodal image data have found widespread applications in visual-based building façade damage detection in recent years, offering comprehensive inspection of façade surfaces with the assistance of drones and infrared thermography. However, the comprehensive integration of such complementary data has been hindered by low levels of automation due to the absence of properly developed methods, resulting in high cost and low efficiency. Thus, this paper proposes an automatic end-to-end building façade damage detection method by integrating multimodal image registration, infrared–visible image fusion (IVIF), and damage segmentation. An infrared and visible image dataset consisting of 1761 pairs encompassing 4 main types of façade damage has been constructed for processing and training. A novel infrared–visible image registration method using main orientation assignment for feature point extraction is developed, reaching a high RMSE of 14.35 to align the multimodal images. Then, a deep learning-based infrared–visible image fusion (IVIF) network is trained to preserve damage characteristics between the modalities. For damage detection, a relatively high mean average precision (mAP) result of 85.4% is achieved by comparing four instance segmentation models, affirming the effective utilization of IVIF results. Full article

This study highlights the importance of unmanned aerial vehicle (UAV) multispectral (MS) imagery for the accurate delineation and analysis of wetland ecosystems, which is crucial for their conservation and management. We present an enhanced semantic segmentation algorithm designed for UAV MS imagery, which incorporates thermal infrared (TIR) data to improve segmentation outcomes. Our approach, involving meticulous image preprocessing, customized network architecture, and iterative training procedures, aims to refine wetland boundary delineation. The algorithm demonstrates strong segmentation results, including a mean pixel accuracy (MPA) of 90.35% and a mean intersection over union (MIOU) of 73.87% across different classes, with a pixel accuracy (PA) of 95.42% and an intersection over union (IOU) of 90.46% for the wetland class. The integration of TIR data with MS imagery not only enriches the feature set for segmentation but also, to some extent, helps address data imbalance issues, contributing to a more refined ecological analysis. This approach, along with the development of a comprehensive dataset that reflects the diversity of wetland environments and advances the utility of remote sensing technologies in ecological monitoring. This research lays the groundwork for more detailed and informative UAV-based evaluations of wetland health and integrity. Full article