Search Results (29)

This study’s primary goal is to use computer vision and ultra-wideband (UWB) localisation techniques to automatically mark numerals in cow photos. In order to accomplish this, we created a UWB-based cow localisation system that involves installing tags on cow heads and placing several base stations throughout the farm. The system can determine the distance between each base station and the cow using wireless communication technology, which allows it to determine the cow’s current location coordinates. The study employed a neural network to train and optimise the ranging data gathered in the 1–20 m range in order to solve the issue of significant ranging errors in conventional UWB positioning systems. The experimental data indicates that the UWB positioning system’s unoptimized range error has an absolute mean of 0.18 m and a standard deviation of 0.047. However, when using a neural network-trained model, the ranging error is much decreased, with an absolute mean of 0.038 m and a standard deviation of 0.0079. The average root mean square error (RMSE) of the positioning coordinates is decreased to 0.043 m following the positioning computation utilising the optimised range data, greatly increasing the positioning accuracy. This study used the conventional camera shooting method for image acquisition. Following image acquisition, the system extracts the cow’s coordinate information from the image using a perspective transformation method. This allows for accurate cow identification and number labelling when compared to the location coordinates. According to the trial findings, this plan, which integrates computer vision and UWB positioning technologies, achieves high-precision cow labelling and placement in the optimised system and greatly raises the degree of automation and precise management in the farming process. This technology has many potential applications, particularly in the administration and surveillance of big dairy farms, and it offers a strong technical basis for precision farming. Full article

There are multiple uses for single-channel images, such as infrared imagery, depth maps, and others. To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. This study explores the application of deep learning techniques for the recognition of wild animals using infrared images. Traditional methods of wildlife monitoring often rely on visible light imaging, which can be hindered by various environmental factors such as darkness, fog, and dense foliage. In contrast, infrared imaging captures the thermal signatures of animals, providing a robust alternative for wildlife detection and identification. We test a Convolutional Neural Network (CNN) model specifically designed to analyze infrared images, leveraging the unique thermal patterns emitted by different animal species. The model is trained and tested on a diverse dataset of infrared images, demonstrating high accuracy in distinguishing between multiple species. In this paper, we also present a comparison of several well-known artificial neural networks on this data. To ensure accurate testing, we introduce a new dataset containing infrared photos of Slovak wildlife, specifically including classes such as bear, deer, boar, and fox. To complement this dataset, the Fashion MNIST dataset was also used. Our results indicate that deep learning approaches significantly enhance the capability of infrared imaging for wildlife monitoring, offering a reliable and efficient tool for conservation efforts and ecological studies. Full article

The large number of images in the different areas and the possibilities of technologies lead to various solutions in automatization using image data. In this paper, tattoo detection and identification were analyzed. The combination of YOLOv5 object detection methods and similarity measures was investigated. During the experimental research, various parameters have been investigated to determine the best combination of parameters for tattoo detection. In this case, the influence of data augmentation parameters, the size of the YOLOv5 models (n, s, m, l, x), and the three main hyperparameters of YOLOv5 were analyzed. Also, the efficiency of the most popular similarity distances cosine and Euclidean was analyzed in the tattoo identification process with the purpose of matching the detected tattoo with the person’s tattoo in the database. Experiments have been performed using the deMSI dataset, where images were manually labeled to be suitable for use by the YOLOv5 algorithm. To validate the results obtained, the newly collected tattoo dataset was used. The results have shown that the highest average accuracy of all tattoo detection experiments has been obtained using the YOLOv5l model, where [email protected]:0.95 is equal to 0.60, and [email protected] is equal to 0.79. The accuracy for tattoo identification reaches 0.98, and the F-score is up to 0.52 when the highest cosine similarity tattoo is associated. Meanwhile, to ensure that no suspects will be missed, the cosine similarity threshold value of 0.15 should be applied. Then, photos with higher similarity scores should be analyzed only. This would lead to a 1.0 recall and would reduce the manual tattoo comparison by 20%. Full article

Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detection and prevention by focusing on the system’s design and the novel application of AI classifiers within an integrated framework. Our primary aim is to evaluate the feasibility and applicability of such a framework in a real-world application context. The proposed approach is shown to tackle the pervasive issue of cyberbullying effectively. Full article

Detection of air bubbles in fluidic channels plays a fundamental role in all that medical equipment where liquids flow inside patients’ blood vessels or bodies. In this work, we propose a multi-parameter sensing system for simultaneous recognition of the fluid, on the basis of its refractive index and of the air bubble transit. The selected optofluidic platform has been designed and studied to be integrated into automatic pumps for the administration of commercial liquid. The sensor includes a laser beam that crosses twice a plastic cuvette, provided with a back mirror, and a position-sensitive detector. The identification of fluids is carried out by measuring the displacement of the output beam on the detector active surface and the detection of single air bubbles can be performed with the same instrumental scheme, exploiting a specific signal analysis. When a bubble, traveling along the cuvette, crosses the readout light beam, radiation is strongly scattered and a characteristic fingerprint shape of the photo-detected signals versus time is clearly observed. Experimental testing proves that air bubbles can be successfully detected and counted. Their traveling speed can be estimated while simultaneously monitoring the refractive index of the fluid. Full article

Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a system combining voice spoof detection and algorithm recognition. In contrast, the generalizability of the spoof detection model is discussed from the perspective of embedding space and decision boundaries to face the voice spoofing attacks generated by spoof algorithms that are not available in the training set. This study presents a method for voice spoof algorithms recognition based on incremental learning, taking into account data flow scenarios where new spoof algorithms keep appearing in reality. Our experimental results on the LA dataset of ASVspoof show that our system can improve the generalization of spoof detection and identify new voice spoof algorithms without catastrophic forgetting. Full article

In close-range photogrammetry, circular-coded targets (CCTs) are a reliable method to solve the issue of image correspondence. Currently, the identification methods for CCTs are very mature, but complex illumination conditions are still a key factor restricting identification. This article proposes an adaptive homomorphic filtering (AHF) algorithm to solve this issue, utilizing homomorphic filtering (HF) to eliminate the influence of uneven illumination. However, HF parameters vary with different lighting types. We use a genetic algorithm (GA) to carry out global optimization and take the identification result as the objective function to realize automatic parameter adjustment. This is different from the optimization strategy of traditional adaptive image enhancement methods, so the most significant advantage of the proposed algorithm lies in its automation and universality, i.e., users only need to input photos without considering the type of lighting conditions. As a preprocessing algorithm, we conducted experiments combining advanced commercial photogrammetric software and traditional identification methods, respectively. We cast stripe- and lattice-structured light to create complex lighting conditions, including uneven lighting, dense shadow areas, and elliptical light spots. Experiments showed that our algorithm significantly improves the robustness and accuracy of CCT identification methods under complex lighting conditions. Given the perfect performance under stripe-structured light, this algorithm can provide a new idea for the fusion of close-range photogrammetry and structured light. This algorithm helps to improve the quality and accuracy of photogrammetry and even helps to improve the decision making and planning process of photogrammetry. Full article

Pathologies in concrete structures, such as cracks, splintering, efflorescence, corrosion spots, and exposed steel bars, can be visually evidenced on the concrete surface. This paper proposes a method for automatically detecting these pathologies from images of the concrete structure. The proposed method uses deep neural networks to detect pathologies in these images. This method results in time savings and error reduction. The paper presents results in detecting the pathologies from wide-angle images containing the overall structure and also for the specific pathology identification task for cropped images of the region of the pathology. Identifying pathologies in cropped images, the classification task could be performed with 99.4% accuracy using cross-validation and classifying cracks. Wide images containing no, one, or several pathologies in the same image, the case of pathology detection, could be analyzed with the YOLO network to identify five pathology classes. The results for detection with YOLO were measured with mAP, mean Average Precision, for five classes of concrete pathology, reaching 11.80% for fissure, 19.22% for fragmentation, 5.62% for efflorescence, 27.24% for exposed bar, and 24.44% for corrosion. Pathology identification in concrete photos can be optimized using deep learning. Full article

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease’s impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models. Full article

The effects of climate change are causing an increase in the frequency and extent of natural disasters. Because of their morphological characteristics, rivers can cause major flooding events. Indeed, they can be subjected to variations in discharge in response to heavy rainfall and riverbank failures. Among the emerging methodologies that address the monitoring of river flooding, those that include the combination of Unmanned Aerial Vehicle (UAV) and photogrammetric techniques (i.e., Structure from Motion-SfM) ensure the high-frequency acquisition of high-resolution spatial data over wide areas and so the generation of orthomosaics, useful for automatic feature extraction. Trainable Weka Segmentation (TWS) is an automatic feature extraction open-source tool. It was developed to primarily fulfill supervised classification purposes of biological microscope images, but its usefulness has been demonstrated in several image pipelines. At the same time, there is a significant lack of published studies on the applicability of TWS with the identification of a universal and efficient combination of machine learning classifiers and segmentation approach, in particular with respect to classifying UAV images of riverine environments. In this perspective, we present a study comparing the accuracy of nine combinations, classifier plus image segmentation filter, using TWS, also with respect to human photo-interpretation, in order to identify an effective supervised approach for automatic river features extraction from UAV multi-temporal orthomosaics. The results, which are very close to human interpretation, indicate that the proposed approach could prove to be a valuable tool to support and improve the hydro-geomorphological and flooding hazard assessments in riverine environments. Full article

Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes. One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV. It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model. In this paper, we discuss the collection of images for three popular UAVs at different elevations and different distances from the observer, and using different camera zoom levels. We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset and the concept of transfer learning from the well-known ImageNet database. The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer. This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g., UAVs carrying loads). Full article

Automatic age estimation from facial images is an exciting machine learning topic that has attracted researchers’ attention over the past several years. Numerous human–computer interaction applications, such as targeted marketing, content access control, or soft-biometrics systems, employ age estimation models to carry out secondary tasks such as user filtering or identification. Despite the vast array of applications that could benefit from automatic age estimation, building an automatic age estimation system comes with issues such as data disparity, the unique ageing pattern of each individual, and facial photo quality. This paper provides a survey on the standard methods of building automatic age estimation models, the benchmark datasets for building these models, and some of the latest proposed pieces of literature that introduce new age estimation methods. Finally, we present and discuss the standard evaluation metrics used to assess age estimation models. In addition to the survey, we discuss the identified gaps in the reviewed literature and present recommendations for future research. Full article

A weak interlayer is the key factor in controlling slope stability. It is of great significance to effectively identify the weak interlayer in the study of spatial and temporal distribution law and the internal structure characteristics of a landslide. Considering the limitations of traditional optical imaging and wave speed test methods, this paper presents a weak interlayer identification method based on borehole photo-acoustic combination measurement. By using the combination of optical imaging and acoustic wave scanning, the multi-source data collection of borehole rock wall and borehole surrounding rock is realized, which effectively captures the comprehensive response characteristics of the weak interlayer. This paper first constructs a multi-source data acquisition technology based on the borehole photo-acoustic combination measurement to realize the visualization of the image information and acoustic data of the target area on the borehole rock wall. Subsequently, the optical image features and the acoustic response characteristics of the weak interlayer are clarified based on the optical image and the acoustic scanning data. The hole wall texture characteristic response function, hole wall integrity characteristic response function, hole wall acoustic characteristic response function and hole wall contour characteristic response function are constructed. Finally, the landslide weak interlayer identification method considering the texture characteristics, complete characteristics, acoustic response characteristics and contour characteristics of the borehole rock wall is proposed, which effectively distinguishes the types of rock mass structural surface and realizes the automatic identification of the weak interlayer. Combined with the case analysis, the correctness and reliability of the present method are verified. The results show that the method can identify the weak interlayer and provide scientific basis for landslide management, which can provide a feasible and effective new way to identify the landslide weak interlayer in practical engineering, with a good application prospect and promotion value. Full article

Locating and calculating the number of asbestos-cement corrugated roofing tiles is the first step in the demolition process. In this work, archived image cubes of Taiwan served as the fundamental data source used via machine learning approach to identify the existence of asbestos-cement corrugated roofing tiles with more than 85% accuracy. An adequate quantity of ground-truth data covering all the types of roofs via aerial hyperspectral scan was the key to success for this study. Twenty randomly picked samples from the ground-truth group were examined by X-ray refraction detection to ensure correct identification of asbestos-cement corrugated roofing tiles with remote sensing. To improve the classifying accuracy ratio, two different machine learning algorithms were applied to gather the target layers individually using the same universal training model established from 400 ground-truth samples. The agreement portions within the overlapping layers of these two approaches were labeled as the potential targets, and the pixel growth technique was performed to detect the roofing boundary and create the polygon layer with size information. Exacting images from aerial photos within the chosen polygon were compared to up-to-date Sentinel-1 images to find the temporal disagreements and remove the mismatched buildings, identified as non-asbestos roofs, from the database to reflect the actual condition of present data. This automatic matching could be easily performed by machine learning to resolve the information lag while using archived data, which is an essential issue when detecting targets with non-simultaneous acquired images over a large area. To meet the 85% kappa accuracy requirement, the recurring processes were applied to find the optimal parameters of the machine learning model. Meanwhile, this study found that the support vector machine method was easier to handle, and the convolution neuro network method offered better accuracy in automatic classification with a universal training model for vast areas. This work demonstrated a feasible approach using low-cost and low-resolution archived images to automatically detect the existence of asbestos-cement corrugated roofing tiles over large regions. The entire work was completed within 16 months for an area of 36,000 km², and the detected number of asbestos-cement corrugated roofing tiles was more than three times the initial estimation by statistics method from two small-area field surveys. Full article

The precision fish farming concept has been widely investigated in research and is highly desirable in aquaculture as it creates opportunities for precisely controlling and monitoring fish cultivation processes and increasing fish welfare. The automatic identification of individual fish could be one of the keys to enabling individual fish treatment. In a previous study, we already demonstrated that the visible patterns on a fish’s body can be used for the non-invasive individual identification of fishes from the same species (with obvious skin patterns, such as salmonids) over long-term periods. The aim of this study was to verify the possibility of using fully-automatic non-invasive photo-identification of individual fish based on natural marks on the fish’s body without any obvious skin patterns. This approach is an alternative to stressful invasive tagging and marking techniques. Scale patterns on the body and operculum, as well as lateral line shapes, were used as discriminative features for the identification of individuals in a closed group of fish. We used two fish species: the European seabass Dicentrarchus labrax and the common carp Cyprinus carpio. The identification method was tested on four experimental data sets for each fish species: two separate short-term data sets (pattern variability test) and two long-term data sets (pattern stability test) for European seabass (300 individual fish) and common carp (32 individual fish). The accuracy of classification was 100% for both fish species in both the short-term and long-term experiments. According to these results, the methods used for automatic non-invasive image-based individual-fish identification can also be used for fish species without obvious skin patterns. Full article