Search Results (111)

Surface roughness, interpreted in the wide sense of surface texture, is a generic term referring to a variety of aspects and scales of spatial variability of surfaces. The analysis of solid earth surface roughness is useful for understanding, characterizing, and monitoring geomorphic factors at multiple spatiotemporal scales. The different geomorphic features characterizing a landscape exhibit specific characteristics and scales of surface texture. The capability to selectively analyze specific roughness metrics at multiple spatial scales represents a key tool in geomorphometric analysis. This research presents a simplified geostatistical approach for the multiscale analysis of surface roughness, or of image texture in the case of images, that is highly informative and interpretable. The implemented approach is able to describe two main aspects of short-range surface roughness: omnidirectional roughness and roughness anisotropy. Adopting simple upscaling approaches, it is possible to perform a multiscale analysis of roughness. An overview of the information extraction potential of the approach is shown for the analysis of a portion of the Taklimakan desert (China) using a 30 m resolution DEM derived from the Copernicus Glo-30 DSM. The multiscale roughness indexes are used as input features for unsupervised and supervised learning tasks. The approach can be refined both from the perspective of the multiscale analysis as well as in relation to the surface roughness indexes considered. However, even in its present, simplified form, it can find direct applications in relation to multiple contexts and research topics. Full article

Affected by the complex underwater environment and the limitations of low-resolution sonar image data and small sample sizes, traditional image recognition algorithms have difficulties achieving accurate sonar image recognition. The research builds on YOLOv7 and devises an innovative fast recognition model designed explicitly for sonar images, namely the Dual Attention Mechanism YOLOv7 model (DA-YOLOv7), to tackle such challenges. New modules such as the Omni-Directional Convolution Channel Prior Convolutional Attention Efficient Layer Aggregation Network (OA-ELAN), Spatial Pyramid Pooling Channel Shuffling and Pixel-level Convolution Bilat-eral-branch Transformer (SPPCSPCBiFormer), and Ghost-Shuffle Convolution Enhanced Layer Aggregation Network-High performance (G-ELAN-H) are central to its design, which reduce the computational burden and enhance the accuracy in detecting small targets and capturing local features and crucial information. The study adopts transfer learning to deal with the lack of sonar image samples. By pre-training the large-scale Underwater Acoustic Target Detection Dataset (UATD dataset), DA-YOLOV7 obtains initial weights, fine-tuned on the smaller Smaller Common Sonar Target Detection Dataset (SCTD dataset), thereby reducing the risk of overfitting which is commonly encountered in small datasets. The experimental results on the UATD, the Underwater Optical Target Detection Intelligent Algorithm Competition 2021 Dataset (URPC), and SCTD datasets show that DA-YOLOV7 exhibits outstanding performance, with [email protected] scores reaching 89.4%, 89.9%, and 99.15%, respectively. In addition, the model maintains real-time speed while having superior accuracy and recall rates compared to existing mainstream target recognition models. These findings establish the superiority of DA-YOLOV7 in sonar image analysis tasks. Full article

In order to accurately estimate the distribution of Trachinotus ovatus in marine cages, a novel method was developed using omnidirectional scanning sonar and deep-learning techniques. This method involved differentiating water layers and clustering data layer by layer to achieve precise location estimation. The approach comprised two main components: fish identification and fish clustering. Firstly, omnidirectional scanning sonar was employed to perform spiral detection within marine cages, capturing fish image data. These images were then labeled to construct a training dataset for an enhanced CS-YOLOv8s model. After training, the CS-YOLOv8s model was used to identify and locate fish within the images. Secondly, the cages were divided into water layers with depth intervals of 40 cm. The identification coordinate data for each water layer were clustered using the DBSCAN method to generate location coordinates for the fish in each layer. Finally, the coordinate data from all water layers were consolidated to determine the overall distribution of fish within the cage. This method was shown, through multiple experimental results, to effectively estimate the distribution of Trachinotus ovatus in marine cages, closely matching the distributions detected manually. Full article

In the field of endoscopic imaging, challenges such as low resolution, complex textures, and blurred edges often degrade the quality of 3D reconstructed models. To address these issues, this study introduces an innovative endoscopic image super-resolution and 3D reconstruction technique named Omni-Directional Focus and Scale Resolution (OmDF-SR). This method integrates an Omnidirectional Self-Attention (OSA) mechanism, an Omnidirectional Scale Aggregation Group (OSAG), a Dual-stream Adaptive Focus Mechanism (DAFM), and a Dynamic Edge Adjustment Framework (DEAF) to enhance the accuracy and efficiency of super-resolution processing. Additionally, it employs Structure from Motion (SfM) and Multi-View Stereo (MVS) technologies to achieve high-precision medical 3D models. Experimental results indicate significant improvements in image processing with a PSNR of 38.2902 dB and an SSIM of 0.9746 at a magnification factor of ×2, and a PSNR of 32.1723 dB and an SSIM of 0.9489 at ×4. Furthermore, the method excels in reconstructing detailed 3D models, enhancing point cloud density, mesh quality, and texture mapping richness, thus providing substantial support for clinical diagnosis and surgical planning. Full article

For accurate urban planning, three-dimensional (3D) building models with a high level of detail (LOD) must be developed. However, most large-scale 3D building models are limited to a low LOD of 1–2, as the creation of higher LOD models requires the modeling of detailed building elements such as walls, windows, doors, and roof shapes. This process is currently not automated and is performed manually. In this study, an end-to-end framework for the creation of 3D building models was proposed by integrating multi-source data such as omnidirectional images, building footprints, and aerial photographs. These different data sources were matched with the building ID considering their spatial location. The building element information related to the exterior of the building was extracted, and detailed LOD3 3D building models were created. Experiments were conducted using data from Kobe, Japan, yielding a high accuracy for the intermediate processes, such as an 86.9% accuracy in building matching, an 88.3% pixel-based accuracy in the building element extraction, and an 89.7% accuracy in the roof type classification. Eighty-one LOD3 3D building models were created in 8 h, demonstrating that our method can create 3D building models that adequately represent the exterior information of actual buildings. Full article

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model’s adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling. Full article

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target’s position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR’s real-time point cloud data are filtered based on position–direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target’s position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function. Full article

Omnidirectional images have gained significant popularity and drawn great attention nowadays, which poses challenges to omnidirectional image processing in solving the bottleneck of storage and transmission. Projecting onto a two-dimensional image plane is generally used to compress an omnidirectional image. However, the most commonly used projection format, the equirectangular projection (ERP), results in a significant amount of redundant samples in the polar areas, thus incurring extra bitrate and geometric distortion. We derive the optimal latitude-adaptive bit allocation for each image tile. Subsequently, we propose a greedy algorithm for non-negative integer bit allocation (NNIBA) for non-uniform quantization under an omnidirectional image quality metric WMSE. In our experiment, we design quantization tables based on JPEG and compare our approach with other sampling-related methods. Our method achieves an average bit saving of 7.9% compared with JPEG while outperforming other sampling-related methods. Besides, we compare our non-uniform quantization approach with two proposed bit allocation methods, achieving an average improvement of 0.35 dB and 2.66 dB under WS-PSNR, respectively. The visual quality assessment also confirms the superiority of our method. Full article

This study used real-time image processing to realize obstacle avoidance and indoor navigation with an omnidirectional wheeled mobile robot (WMR). The distance between an obstacle and the WMR was obtained using a depth camera. Real-time images were used to control the robot’s movements. The WMR can extract obstacle distance data from a depth map and apply fuzzy theory to avoid obstacles in indoor environments. A fuzzy control system was integrated into the control scheme. After detecting a doorknob, the robot could track the target and open the door. We used the speeded up robust features matching algorithm to recognize the WMR’s movement direction. The proposed control scheme ensures that the WMR can avoid obstacles, move to a designated location, and open a door. Like humans, the robot performs the described task only using visual sensors. Full article

The evolution of cameras and LiDAR has propelled the techniques and applications of three-dimensional (3D) reconstruction. However, due to inherent sensor limitations and environmental interference, the reconstruction process often entails significant texture noise, such as specular highlight, color inconsistency, and object occlusion. Traditional methodologies grapple to mitigate such noise, particularly in large-scale scenes, due to the voluminous data produced by imaging sensors. In response, this paper introduces an omnidirectional-sensor-system-based texture noise correction framework for large-scale scenes, which consists of three parts. Initially, we obtain a colored point cloud with luminance value through LiDAR points and RGB images organization. Next, we apply a voxel hashing algorithm during the geometry reconstruction to accelerate the computation speed and save the computer memory. Finally, we propose the key innovation of our paper, the frame-voting rendering and the neighbor-aided rendering mechanisms, which effectively eliminates the aforementioned texture noise. From the experimental results, the processing rate of one million points per second shows its real-time applicability, and the output figures of texture optimization exhibit a significant reduction in texture noise. These results indicate that our framework has advanced performance in correcting multiple texture noise in large-scale 3D reconstruction. Full article

Plant diversity includes over 300,000 species, and leaf structure is one of the main targets of selection, being highly variable in shape and size. On the other hand, the optimization of antenna design has no unique solution to satisfy the current range of applications. We analyzed the foliar geometries of 100 plant species and applied them as a biomimetic design template for microstrip patch antenna systems. From this set, a subset of seven species were further analyzed, including species from tropical and temperate forests across the phylogeny of the Angiosperms. Foliar geometry per species was processed by image processing analyses, and the resultant geometries were used in simulations of the reflection coefficients and the radiation patterns via finite differences methods. A value below −10 dB is set for the reflection coefficient to determine the operation frequencies of all antenna elements. All species showed between 3 and 15 operational frequencies, and four species had operational frequencies that included the 2.4 and 5 GHz bands. The reflection coefficients and the radiation patterns in most of the designs were equal or superior to those of conventional antennas, with several species showing multiband effects and omnidirectional radiation. We demonstrate that plant structures can be used as a biomimetic tool in designing microstrip antenna for a wide range of applications. Full article

This paper presents a CoPlanar-Waveguide (CPW)-fed stingray-shaped Ultra-WideBand (UWB) Multiple-Input–Multiple-Output (MIMO) antenna system designed for microwave imaging applications. Featuring a diagonal square with four inner lines and a vertical line at the center from toe to tip with a CPW feed line, the unit antenna element looks like a stingray fish skeleton and is, therefore, named as a stingray-shaped antenna. It offers a bandwidth spanning from 3.8 to 12.7 GHz. Fabricated on a 31mil RO5880 RF teflon substrate with a relative permittivity of 2.2, the proposed antenna has dimensions of 26 × 29 × 0.787 mm${}^3$. The maximum realized gain achieved is 3.5 dBi with stable omnidirectional radiation patterns. The antenna element is used in a four-antenna MIMO configuration with an isolation-improving structure at the center. The MIMO system has dimensions of 58 × 58 × 0.787 mm${}^3$ with a maximum realized gain of 5.3 dBi. The antenna’s performance in terms of MIMO parameters like Envelope Correlation Coefficient (ECC) and Diversity Gain (DG) is within satisfactory limits for medical imaging applications. Time domain analysis also yields positive results, allowing its integration into a breast phantom tumor detection simulation. The simulation and measurement results demonstrate excellent agreement, making this antenna a promising candidate for microwave imaging and biomedical applications. Full article

A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive’s entrance. Since traffic at the hive’s entrance is a contributing factor to the hive’s productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees manually labeled in 5819 images from 10 randomly selected videos and manually evaluated the trained models on 3600 images from 120 randomly selected videos from different apiaries, years, and queen races. We designed a new energy efficacy metric as a ratio of performance units per energy unit required to make a model operational in a continuous hive monitoring data pipeline. In terms of accuracy, YOLOv3 was first, YOLOv7-tiny—second, and YOLOv4-tiny—third. All models underestimated the true amount of traffic due to false negatives. YOLOv3 was the only model with no false positives, but had the lowest energy efficacy and highest operational energy footprint in a deployed hive monitoring data pipeline. YOLOv7-tiny had the highest energy efficacy and the lowest operational energy footprint in the same pipeline. Consequently, YOLOv7-tiny is a model worth considering for training on larger bee datasets if a primary objective is the discovery of non-invasive computer vision models of traffic quantification with higher energy efficacies and lower operational energy footprints. Full article

In this study, an IoT-based fire detection system was developed to detect and prevent damage from forest fires at an early stage. In Korea, forest fires spread quickly due to the dry climate and winds in spring and autumn, so quick detection and prevention is necessary. To quickly detect and prevent forest fires that occur periodically, a real-time fire detection system was developed by combining a Raspberry Pi and a fisheye camera. A lens with a 220° angle of view was installed, and an image analysis algorithm was implemented using the OpenCV library. The location of the fire was estimated by calculating the polar coordinates of the omnidirectional images. Using the Wi-Fi communication function of the Raspberry Pi, the acquired continuous images were transmitted to the Firebase database, and the images were analyzed to identify the movement path of the forest fire. The developed system was applied to a mountainous area near the Samcheok Campus of Kangwon National University. As a result of the experiment, when the location of points about 25.9 m (average) away from the observation point was predicted, the positional error was analyzed to be about 1.1 m. If the system is improved in the future, it is expected that it will be able to contribute to the early prevention of forest fires with fast and accurate responses. Full article

A visible–extended shortwave infrared indium gallium arsenide (InGaAs) focal plane array (FPA) detector is the ideal choice for reducing the size, weight and power (SWaP) of infrared imaging systems, especially in low-light night vision and other fields that require simultaneous visible and near-infrared light detection. However, the lower quantum efficiency in the visible band has limited the extensive application of the visible–extended InGaAs FPA. Recently, a novel optical metasurface has been considered a solution for a high-performance semiconductor photoelectric device due to its highly controllable property of electromagnetic wave manipulation. Broadband Mie resonator arrays, such as nanocones and nanopillars designed with FDTD methods, were integrated on a back-illuminated InGaAs FPA as an AR metasurface. The visible–extended InGaAs detector was fabricated using substrate removal technology. The nanostructures integrated into the Vis-SWIR InGaAs detectors could realize a 10–20% enhanced quantum efficiency and an 18.8% higher FPA response throughout the wavelength range of 500–1700 nm. Compared with the traditional AR coating, nanostructure integration has advantages, such as broadband high responsivity and omnidirection antireflection, as a promising route for future Vis-SWIR InGaAs detectors with higher image quality. Full article