To detect the categories and positions of various transformer components in inspection images automatically, this paper proposes a transformer component detection model with high detection accuracy, based on the structure of Faster R-CNN. In consider...
Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice,...
Accurate knowledge of urban forest patterns contributes to well-managed urbanization, but accurate urban tree canopy mapping is still a challenging task because of the complexity of the urban structure. In this paper, a new method that combines doubl...
Defect detection on the surface of the steel strip is essential for the quality assurance of the steel strip. Precise localization and classification, the two significant tasks of defect detection, still need to be completed due to the diversity of d...
Spatially explicit information on cropland use intensity is vital for monitoring land and water resource demands in agricultural systems. Cropping practices underlie substantial spatial and temporal variability, which can be captured through the anal...
Navigating unstructured roads in vineyards with weak satellite signals presents significant challenges for robotic systems. This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such condition...
Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the...
(1) Background: Small objects in Unmanned Aerial Vehicle (UAV) images are often scattered throughout various regions of the image, such as the corners, and may be blocked by larger objects, as well as susceptible to image noise. Moreover, due to thei...
It has been widely argued that morphological competence, particularly functional morphology, represents the bottleneck of second language acquisition (Jensen et al. 2017; Lardiere 1998, 2005; Slabakova 2008, 2009, 2013). In this study, we explore thr...
Farmers in the Brazilian Cerrado are increasing grain production by cultivating second crops during the same crop growing season. The release of PlanetScope (PS) satellite images represents an innovative opportunity to monitor double cropping product...
In this paper, we propose a shrinking double inertial framework for approximating common fixed points of a countable family of quasi-nonexpansive mappings in Hilbert spaces. The framework combines a shrinking projection technique with a double inerti...
Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long re...
Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic...
Small targets exist in large numbers in various fields. They are broadly used in aerospace, video monitoring, and industrial detection. However, because of its tiny dimensions and modest resolution, the precision of small-target detection is low, and...
NDVI is an important parameter for environmental assessment and precision agriculture that well-describes the status of vegetation. Nevertheless, the clouds in optical images often result in the absence of NDVI information at key growth stages. The i...
This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using e...
Side-scan sonar systems play an important role in tasks such as marine terrain exploration and underwater target identification. Target segmentation of side-scan sonar images is an effective method of underwater target detection. However, the princip...
To address the limitation of 2D lane detection methods with monocular vision, which fail to capture the three-dimensional position of lane boundaries, this study proposes a convolutional neural network architecture for 3D lane detection. The deep res...
Awns play important roles in seed dispersal, protection against predators, and photosynthesis. The characterization of genes related to the formation of awns helps understand the regulation mechanisms of awn development. In the present study, the &ld...
In this paper, a novel fractional-order discrete map with a sinusoidal function possessing typical nonlinear features, including chaos and bifurcations, is proposed. Firstly, the basic properties involving the stability of the equilibrium points and...
Accurate mapping of rice cropping patterns is fundamental to sustainable agricultural management and regional food security assessment. In this study, we developed a high-precision mapping framework for major rice cropping patterns in the Yangtze Riv...
Timely detection of defects is essential for ensuring safe and stable operation of concrete buildings. Automatic segmentation of concrete buildings’ surfaces is challenging due to the high diversity of crack appearance, the detailed information...
In this study, a vision-based remote and synchronized control scheme is proposed for the double six-DOF robotic manipulators. Using an Intel RealSense D435 depth camera and MediaPipe skeletal image feature technique, the operator’s 3D hand pose...
A lightweight neural network fault diagnosis method based on Gramian angular field (GAF) feature map construction and efficient channel attention (ECA) optimization is presented herein to address the problem of the complex structure of traditional ne...
The accurate acquisition of safflower filament information is the prerequisite for robotic picking operations. To detect safflower filaments accurately in different illumination, branch and leaf occlusion, and weather conditions, an improved Faster R...
In the process of knowledge graph construction, entity linking is a pivotal step, which maps mentions in text to a knowledge base. Existing models only utilize individual information to represent their latent features and ignore the correlation betwe...
Temporal action localization (TAL) aims to predict action instance categories in videos and identify their start and end times. However, existing Transformer-based backbones focus only on global or local features, resulting in the loss of information...
Deep learning methods have gained significant popularity in the field of polarimetric synthetic aperture radar (PolSAR) image classification. These methods aim to extract high-level semantic features from the original PolSAR data to learn the polarim...
In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network...
Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based de...
Deep-learning methods, especially convolutional neural networks (CNN), have become the first choice for hyperspectral image (HSI) classification to date. It is a common procedure that small cubes are cropped from hyperspectral images and then fed int...
In this paper, we propose a new method of land use and land cover classification for polarimetric SAR data. This algorithm consists of three parts. First, the multiple-component model-based scattering decomposition technique is improved and the decom...
Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally,...
Accurate and timely acquisition of cropping intensity and spatial distribution of paddy rice is not only an important basis for monitoring growth and predicting yields, but also for ensuring food security and optimizing the agricultural production ma...
This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking...
A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous...
In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissanc...
The accurate and automated delineation of Field Parcels (FPs) serves as the foundation for modern precision agriculture. While deep learning-based extraction from high-resolution remote sensing imagery has improved pixel-level accuracy, current metho...
This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterráneo de Canfranc (LSC), Spain. The detector is based on a pla...
Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more com...
The primary objective of this study is the computation of the matching polynomials of a number of symmetric, semisymmetric, double group graphs, and solids in third and higher dimensions. Such computations of matching polynomials are extremely challe...
Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which f...
Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of...
Accurately tracking the changes in rice cropping intensity is a critical requirement for policymakers to formulate reasonable land-use policies. Southern China is a traditional region for rice multi-cropping, yet less is known about its spatial&ndash...
In view of the low accuracy and slow speed of goat-face recognition in real breeding environments, dairy goats were taken as the research objects, and video frames were used as the data sources. An improved YOLOv4 goat-face-recognition model was prop...
Deep convolutional neural networks (CNNs) have been successfully applied to spectral reconstruction (SR) and acquired superior performance. Nevertheless, the existing CNN-based SR approaches integrate hierarchical features from different layers indis...
Accurate object detection is a fundamental component of autonomous apple-picking systems. In response to the insufficient recognition performance and poor generalization capacity of existing detection algorithms under unstructured orchard scenarios,...
Ice rumples are locally-grounded features of flowing ice shelves, elevated tens of meters above the surrounding surface. These features may significantly impact the dynamics of ice-shelf grounding lines, which are strongly related to shelf stability....
In the application scenarios of quadrotors, it is expected that only part of the obstacles can be identified and located in advance. In order to make quadrotors fly safely in this situation, we present a deep reinforcement learning-based framework to...
To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algori...
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