Search Results (2,782)

Recent advancements in image processing technology have positively impacted some fields, such as image, document, and video production. However, the negative implications of these advancements have also increased, with document image manipulation being a prominent issue. Document image manipulation involves the forgery or alteration of documents like receipts, invoices, various certificates, and confirmations. The use of such manipulated documents can cause significant economic and social disruption. To prevent these issues, various methods for the detection of forged document images are being researched, with recent proposals focused on deep learning techniques. An essential aspect of using deep learning to detect manipulated documents is to enhance or augment the characteristics of document images before inputting them into a model. Enhancing the distinctive features of manipulated documents before inputting them into a deep learning model is crucial to achieve high accuracy. One crucial characteristic of document images is their inherent symmetrical patterns, such as consistent text alignment, structural balance, and uniform pixel distribution. This study investigates document forgery detection through a symmetry-aware approach. By focusing on the symmetric structures found in document layouts and pixel distribution, the proposed LTHE technique enhances feature extraction in deep learning-based models. Therefore, this study proposes a new image enhancement technique based on the results of three general-purpose CNN models to enhance the characteristics of document images and achieve high accuracy in deep learning-based forgery detection. The proposed LTHE (Log-Transform Histogram Equalization) technique increases low pixel values through log transformation and increases image contrast by performing histogram equalization to make the features of the image more prominent. Experimental results show that the proposed LTHE technique achieves higher accuracy when compared to other enhancement methods, indicating its potential to aid the development of deep learning-based forgery detection algorithms in the future. Full article

This article introduces an innovative meteor detection system that integrates high-speed photodiode detectors with traditional camera-based systems. The system employs four photodiodes to record changes in sky brightness at 100 Hz, enabling meteor detection and the observation of their dynamics. This technology serves as a valuable complement to existing imaging techniques, offering a cost-effective solution for measuring meteor ablation at frequencies beyond the capabilities of camera-based systems. We showcase findings from the Perseid meteor shower, demonstrating the potential of our system. Moreover, our system addresses the current limitations in meteor radiometry, where many existing instruments either remain in developmental stages or have not been validated with a substantial number of confirmed meteor events. Our approach successfully addresses these limitations, demonstrating effectiveness across multiple meteor events simultaneously recorded on video. Full article

Ultrasound (US) remains the main modality for the differential diagnosis of changes revealed by mammography. However, the US images themselves are subject to various types of noise and artifacts from reflections, which can worsen the quality of their analysis. Deep learning methods have a number of disadvantages, including the often insufficient substantiation of the model, and the complexity of collecting a representative training database. Therefore, it is necessary to develop effective algorithms for the segmentation, classification, and analysis of US images. The aim of the work is to develop a method for the automated detection of pathological lesions in breast US images and their segmentation. A method is proposed that includes two stages of video image processing: (1) searching for a region of interest using a random forest classifier, which classifies normal tissues, (2) selecting the contour of the lesion based on the difference in brightness of image pixels. The test set included 52 ultrasound videos which contained histologically proven suspicious lesions. The average frequency of lesion detection per frame was 91.89%, and the average accuracy of contour selection according to the IoU metric was 0.871. The proposed method can be used to segment a suspicious lesion. Full article

Nowadays, object detection algorithms are widely used in various scenarios. However, there are further small object detection requirements in some special scenarios. Due to the problems related to small objects, such as their less available features, unbalanced samples, higher positioning accuracy requirements, and fewer data sets, a small object detection algorithm is more complex than a general object detection algorithm. The detection effect of the model for small objects is not ideal. Therefore, this paper takes YOLOXs as the benchmark network and enhances the feature information on small objects by improving the network’s structure so as to improve the detection effect of the model for small objects. This specific research is presented as follows: Aiming at the problem of a neck network based on an FPN and its variants being prone to information loss in the feature fusion of non-adjacent layers, this paper proposes a feature fusion and distribution module, which replaces the information transmission path, from deep to shallow, in the neck network of YOLOXs. This method first fuses and extracts the feature layers used by the backbone network for prediction to obtain global feature information containing multiple-size objects. Then, the global feature information is distributed to each prediction branch to ensure that the high-level semantic and fine-grained information are more efficiently integrated so as to help the model effectively learn the discriminative information on small objects and classify them correctly. Finally, after testing on the VisDrone2021 dataset, which corresponds to a standard image size of 1080p (1920 × 1080), the resolution of each image is high and the video frame rate contained in the dataset is usually 30 frames/second (fps), with a high resolution in time, it can be used to detect objects of various sizes and for dynamic object detection tasks. And when we integrated the module into a YOLOXs network (named the FE-YOLO network) with the three improvement points of the feature layer, channel number, and maximum pool, the mAP and APs were increased by 1.0% and 0.8%, respectively. Compared with YOLOV5m, YOLOV7-Tiny, FCOS, and other advanced models, it can obtain the best performance. Full article

Video cameras are one of the important elements in ensuring security in public areas. Videos inspected by expert personnel using traditional methods may have a high error rate and take a long time to complete. In this study, a new deep learning-based method is proposed for the detection of abandoned objects, such as bags, suitcases, and suitcases left unsupervised in public areas. Transfer learning-based keyframe detection was first performed to remove unnecessary and repetitive frames from the ABODA dataset. Then, human and object classes were detected using the weights of the YOLOv8l model, which has a fast and effective object detection feature. Abandoned object detection is achieved by tracking classes in consecutive frames with the DeepSORT algorithm and measuring the distance between them. In addition, the location information of the human and object classes in the frames was analyzed by a large language model supported by prompt engineering. Thus, an explanation output regarding the location, size, and estimation rate of the object and human classes was created for the authorities. It is observed that the proposed model produces promising results comparable to the state-of-the-art methods for suspicious object detection from videos with success metrics of 97.9% precision, 97.0% recall, and 97.4% f1-score. Full article

Owing to the demand for smoke and flame detection in natural scenes, this paper proposes a lightweight deep learning model, SF-YOLO (Smoke and Fire-YOLO), for video smoke and flame detection in such environments. Firstly, YOLOv11 is employed as the backbone network, combined with the C3k2 module based on a two-path residual attention mechanism, and a target detection head frame with an embedded attention mechanism. This combination enhances the response of the unobscured regions to compensate for the feature loss in occluded regions, thereby addressing the occlusion problem in dynamic backgrounds. Then, a two-channel loss function (W-SIoU) based on dynamic tuning and intelligent focusing is designed to enhance loss computation in the boundary regions, thus improving the YOLOv11 model’s ability to recognize targets with ambiguous boundaries. Finally, the algorithms proposed in this paper are experimentally validated using the self-generated dataset S-Firedata and the public smoke and flame virtual dataset M⁴SFWD. These datasets are derived from internet smoke and flame video frame extraction images and open-source smoke and flame dataset images, respectively. The experimental results demonstrate, compared with deep learning models such as YOLOv8, Gold-YOLO, and Faster-RCNN, the SF-YOLO model proposed in this paper is more lightweight and exhibits higher detection accuracy and robustness. The metrics mAP50 and mAP50-95 are improved by 2.5% and 2.4%, respectively, in the self-made dataset S-Firedata, and by 0.7% and 1.4%, respectively, in the publicly available dataset M⁴SFWD. The research presented in this paper provides practical methods for the automatic detection of smoke and flame in natural scenes, which can further enhance the effectiveness of fire monitoring systems. Full article

The Morris Water Maze (MWM) is a widely used behavioral test to assess the spatial learning and memory of animals, particularly valuable in studying neurodegenerative disorders such as Alzheimer’s disease. Traditional methods for analyzing MWM experiments often face limitations in capturing the complexity of animal behaviors. In this study, we present a novel AI-based automated framework to process and evaluate MWM test videos, aiming to enhance behavioral analysis through machine learning. Our pipeline involves video preprocessing, animal detection using convolutional neural networks (CNNs), trajectory tracking, and postprocessing to derive detailed behavioral features. We propose concentric circle segmentation of the pool next to the quadrant-based division, and we extract 32 behavioral metrics for each zone, which are employed in classification tasks to differentiate between younger and older animals. Several machine learning classifiers, including random forest and neural networks, are evaluated, with feature selection techniques applied to improve the classification accuracy. Our results demonstrate a significant improvement in classification performance, particularly through the integration of feature sets derived from concentric zone analyses. This automated approach offers a robust solution for MWM data processing, providing enhanced precision and reliability, which is critical for the study of neurodegenerative disorders. Full article

The YOLO series is widely recognized for its efficiency in the real-time detection of objects within images and videos. Accurately identifying and classifying fabric types in the textile industry is vital to ensuring quality, managing supply, and increasing customer satisfaction. We developed a method for fabric type classification and object detection using the YOLOv5 architecture. The model was trained on a diverse dataset containing images of different fabrics, including cotton, hanbok, dyed cotton yarn, and a plain cotton blend. We conducted a dataset preparation process, including data collection, annotation, and training procedures for data augmentation to improve model generalization. The model’s performance was evaluated using precision, recall, and F1-score. The developed model detected and classified fabrics with an accuracy of 81.08%. YOLOv5s allowed a faster performance than other models. The model can be used for automated quality control, inventory tracking, and retail analytics. The deep learning-based object detection method with YOLOv5 addresses challenges related to fabric classification, improving the abilities and productivity of manufacturing and operations. Full article

Traffic flow at intersections is influenced by spatial design, control methods, technical equipment, and traffic volume. This article focuses on detecting traffic flows at intersections using video recordings, employing a YOLO-based framework for automated analysis. We compare manual evaluation with machine processing to demonstrate the efficiency improvements in traffic engineering tasks through automated traffic data analysis. The output data include traditionally immeasurable parameters, such as speed and vehicle gaps within the observed intersection area. The traffic analysis incorporates findings from monitoring groups of vehicles, focusing on their formation and speed as they traverse the intersection. Our proposed system for monitoring and classifying traffic flow was implemented at a selected intersection in the city of Zilina, Slovak Republic, as part of a pilot study for this research initiative. Based on evaluations using local data, the YOLOv9c detection model achieved an mAP50 of 98.2% for vehicle localization and classification across three basic classes: passenger cars, trucks, and buses. Despite the high detection accuracy of the model, the automated annotations for vehicle entry and exit at the intersection showed varying levels of accuracy compared to manual evaluation. On average, the mean absolute error between annotations by traffic specialists and the automated framework for the most frequent class, passenger cars, was 2.73 across all directions at 15 min intervals. This indicates that approximately three passenger cars per 15 min interval were either undetected or misclassified. Full article

With the ongoing development of computer vision technologies, the automation of lameness detection in dairy cows urgently requires improvement. To address the challenges of detection difficulties and technological limitations, this paper proposes an automated scoring method for cow lameness that integrates deep learning with keypoint tracking. First, the DeepLabCut tool is used to efficiently extract keypoint features during the walking process of dairy cows, which enables the automated monitoring and output of positional information. Then, the extracted positional data are combined with temporal data to construct a scoring model for cow lameness. The experimental results demonstrate that the proposed method tracks the keypoint of cow movement accurately in visible-light videos and satisfies the requirements for real-time detection. The model classifies the walking states of the cows into four levels, i.e., normal, mild, moderate, and severe lameness (corresponding to scores of 0, 1, 2, and 3, respectively). The detection results obtained in real-world real environments exhibit the high extraction accuracy of the keypoint positional information, with an average error of only 4.679 pixels and an overall accuracy of 90.21%. The detection accuracy for normal cows was 89.0%, with 85.3% for mild lameness, 92.6% for moderate lameness, and 100.0% for severe lameness. These results demonstrate that the application of keypoint detection technology for the automated scoring of lameness provides an effective solution for intelligent dairy management. Full article

Facial asymmetry presents a significant challenge for health practitioners, including physicians, dentists, and physical therapists. Manual measurements often lack the precision needed for accurate assessments, highlighting the appeal of imaging technologies like structured light scanners and photogrammetric systems. However, high-end commercial systems remain cost prohibitive, especially for public health services in developing countries. This study aims to evaluate cell-phone-based photogrammetric methods for generating 3D facial models to detect facial asymmetries. For this purpose, 15 patients had their faces scanned with the ACADEMIA 50 3D scanner, as well as with cell phone images and videos using photogrammetry and videogrammetry, resulting in 3D facial models. Each 3D model (coming from a 3D scanner, photogrammetry, and videogrammetry) was half-mirrored to analyze dissimilarities between the two ideally symmetric face sides using Hausdorff distances between the two half-meshes. These distances were statistically analyzed through various measures and hypothesis tests. The results indicate that, in most cases, both photogrammetric and videogrammetric approaches are as reliable as 3D scanning for detecting facial asymmetries. The benefits and limitations of using images, videos, and 3D scanning are also presented. Full article

Objective: The objective of this study was to early-detect gross motor abnormalities through video detection in Taiwanese infants aged 2–6 months. Background: The current diagnosis of infant developmental delays primarily relies on clinical examinations. However, during clinical visits, infants may show atypical behaviors due to unfamiliar environments, which might not truly reflect their true developmental status. Methods: This study utilized videos of infants recorded in their home environments. Two pediatric neurologists manually annotated these clips to identify whether an infant possessed the characteristics of gross motor delays through an assessment of his/her gross motor movements. Using transfer learning techniques, four pose recognition models, including ViTPose, HRNet, DARK, and UDP, were applied to the infant gross motor dataset. Four machine learning classification models, including random forest, support vector machine, logistic regression, and XGBoost, were used to predict the developmental status of infants. Results: The experimental results of pose estimation and tracking indicate that the ViTPose model provided the best performance for pose recognition. A total of 227 features related to kinematics, motions, and postures were extracted and calculated. A one-way ANOVA analysis revealed 106 significant features that were retained for constructing prediction models. The results show that a random forest model achieved the best performance with an average F1-score of 0.94, a weighted average AUC of 0.98, and an average accuracy of 94%. Full article

With the intensification of global climate change, extreme precipitation events are occurring more frequently, making the monitoring and management of urban flooding a critical global issue. Urban surveillance camera sensor networks, characterized by their large-scale deployment, rapid data transmission, and low cost, have emerged as a key complement to traditional remote sensing techniques. These networks offer new opportunities for high-spatiotemporal-resolution urban flood monitoring, enabling real-time, localized observations that satellite and aerial systems may not capture. However, in low-light environments—such as during nighttime or heavy rainfall—the image features of flooded areas become more complex and variable, posing significant challenges for accurate flood detection and timely warnings. To address these challenges, this study develops an imaging model tailored to flooded areas under low-light conditions and proposes an invariant feature extraction model for flooding areas within surveillance videos. By using extracted image features (i.e., brightness and invariant features of flooded areas) as inputs, a deep learning-based flood segmentation model is built on the U-Net architecture. A new low-light surveillance flood image dataset, named UWs, is constructed for training and testing the model. The experimental results demonstrate the efficacy of the proposed method, achieving an mRecall of 0.88, an mF1_score of 0.91, and an mIoU score of 0.85. These results significantly outperform the comparison algorithms, including LRASPP, DeepLabv3+ with MobileNet and ResNet backbones, and the classic DeepLabv3+, with improvements of 4.9%, 3.0%, and 4.4% in mRecall, mF1_score, and mIoU, respectively, compared to Res-UNet. Additionally, the method maintains its strong performance in real-world tests, and it is also effective for daytime flood monitoring, showcasing its robustness for all-weather applications. The findings of this study provide solid support for the development of an all-weather urban surveillance camera flood monitoring network, with significant practical value for enhancing urban emergency management and disaster reduction efforts. Full article

VID-KIDS (Video-Feedback Interaction Guidance for Depressed Mothers and their Infants) is a positive parenting programme comprising three brief nurse-guided video-feedback sessions (offered in-person or virtually via Zoom) that promote “serve and return” interactions by helping depressed mothers to be more sensitive and responsive to infant cues. We examined whether mothers who received the VID-KIDS programme demonstrated improved maternal–infant interaction quality. The secondary hypotheses examined VID-KIDS’ effects on maternal depression, anxiety, perceived parenting stress, infant developmental outcomes, and infant cortisol patterns. A parallel group randomized controlled trial (n = 140) compared the VID-KIDS programme to standard care controls (e.g., a resource and referral programme). The trial was registered in the US Clinical Trials Registry (number NCT03052374). Outcomes were assessed at baseline, nine weeks post-randomization (immediate post-test), and two months post-intervention. Maternal–infant interaction quality significantly improved for the intervention group with moderate to large effects. These improvements persisted during the post-test two months after the final video-feedback session. No significant group differences were detected for secondary outcomes. This study demonstrated that nurse-guided video-feedback can improve maternal–infant interaction in the context of PPD. These findings are promising, as sensitive and responsive parenting is crucial for promoting children’s healthy development. Full article

Wireless capsule endoscopy (WCE) offers a non-invasive diagnostic alternative for the gastrointestinal tract using a battery-powered capsule. Despite advantages, WCE encounters issues with video quality and diagnostic accuracy, often resulting in missing rates of 1–20%. These challenges stem from weak texture characteristics due to non-Lambertian tissue reflections, uneven illumination, and the necessity of color fidelity. Traditional Retinex-based methods used for image enhancement are suboptimal for endoscopy, as they frequently compromise anatomical detail while distorting color. To address these limitations, we introduce QRNet, a novel quaternion-based Retinex framework. QRNet performs image decomposition into reflectance and illumination components within hypercomplex space, maintaining inter-channel relationships that preserve color fidelity. A quaternion wavelet attention mechanism refines essential features while suppressing noise, balancing enhancement and fidelity through an innovative loss function. Experiments on Kvasir-Capsule and Red Lesion Endoscopy datasets demonstrate notable improvements in metrics such as PSNR (+2.3 dB), SSIM (+0.089), and LPIPS (−0.126). Moreover, lesion segmentation accuracy increases by up to 5%, indicating the framework’s potential for improving early-stage lesion detection. Ablation studies highlight the quaternion representation’s pivotal role in maintaining color consistency, confirming the promise of this advanced approach for clinical settings. Full article