Efficient multi-object tracking (MOT) in satellite videos is crucial for numerous applications, ranging from surveillance to environmental monitoring. Existing methods often struggle with effectively exploring the correlation and contextual cues inhe...
Monitoring of rice planting areas plays an important role in maintaining food security. With powerful automatic feature extraction capability, crop mapping based on deep learning methods has become one of the most important research directions of cro...
The identification of light stress is crucial for light control in plant factories. Image-based lighting classification of leafy vegetables has exhibited remarkable performance with high convenience and economy. Convolutional Neural Network (CNN) has...
Heterogeneous change detection has a wide range of applications in many fields. However, to date, many existing problems of heterogeneous change detection, such as false alarm suppression, have not been specifically addressed. In this article, we dis...
Point cloud processing based on deep learning is developing rapidly. However, previous networks failed to simultaneously extract inter-feature interaction and geometric information. In this paper, we propose a novel point cloud analysis module, CGR-b...
Fine-grained object detection aims to accurately localize the object bounding box while identifying the specific model of the object, which is more challenging than conventional remote sensing object detection. Transformer-based object detector (DETR...
To address the contradiction between watermarking robustness and imperceptibility, a zero-watermarking method based on shrinkage and a redundant feature elimination network (SRFENet) is proposed in this paper. First, in order to have the capability o...
The application of mulching film has significantly contributed to improving agricultural output and benefits, but residual film has caused severe impacts on agricultural production and the environment. In order to realize the accurate recycling of ag...
Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response...
12 January 2023
The emotion of humans is an important indicator or reflection of their mental states, e.g., satisfaction or stress, and recognizing or detecting emotion from different media is essential to perform sequence analysis or for certain applications, e.g.,...
Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot’s pose in three-dimensional space by analyzing the depth variations of inter-frame feature points. Inter-frame feature point mismatches can lead to tracking failure, impac...
The contribution of oil palm plantations to the economic growth of tropical developing countries makes it essential to monitor their expansion into the tropical forest; consequently, most studies focus on improving mapping accuracy while using satell...
This paper presents a mapping method for wide row crop fields. The resulting map shows the crop rows and weeds present in the inter-row spacing. Because field videos are acquired with a camera mounted on top of an agricultural vehicle, a method for i...
The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style. However, these methods have a limitation that the scale-across style pattern of a style image cannot be fully tr...
(1) Background: We assessed the test–re-test repeatability of radiomics in metastatic castration-resistant prostate cancer (mCPRC) bone disease on whole-body diffusion-weighted (DWI) and T1-weighted Dixon MRI. (2) Methods: In 10 mCRPC patients,...
The cross-modal retrieval task can return different modal nearest neighbors, such as image or text. However, inconsistent distribution and diverse representation make it hard to directly measure the similarity relationship between different modal sam...
Fine-grained image classification is faced with the challenge of significant intra-class differences and subtle similarities between classes, with a limited number of labelled data. Previous few-shot learning approaches, however, often fail to recogn...
Urban open spaces (UO) play a crucial role in urban environments, particularly in areas where social and economic activities are rapidly increasing. However, the challenges of high inter-class similarity, complex environmental surroundings, and scale...
Multi-robot systems require collective map information on surrounding environments to efficiently cooperate with one another on assigned tasks. This paper addresses the problem of grid map merging to obtain the collective map information in multi-rob...
Satellite video geographic alignment can be applied to target detection and tracking, true 3D scene construction, image geometry measurement, etc., which is a necessary preprocessing step for satellite video applications. In this paper, a multi-scale...
As one of the most crucial parts of face detection, the accuracy and the generalization of face anti-spoofing are particularly important. Therefore, it is necessary to propose a multi-branch network to improve the accuracy and generalization of the d...
Automatic fingerspelling recognition tackles the communication barrier between deaf and hearing individuals. However, the accuracy of fingerspelling recognition is reduced by high intra-class variability and low inter-class variability. In the existi...
Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical...
In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representat...
The identification of species within an ecosystem plays a key role in formulating an inventory for use in the development of conservation management plans. The classification of mangrove species typically involves intensive field surveys, whereas rem...
Remote sensing image object detection is a challenging task in the field of computer vision due to the complex backgrounds and diverse arrangements of targets in remote sensing images, forming intricate scenes. To overcome this challenge, existing ob...
Dermoscopy images can be classified more accurately if skin lesions or nodules are segmented. Because of their fuzzy borders, irregular boundaries, inter- and intra-class variances, and so on, nodule segmentation is a difficult task. For the segmenta...
Current existing methods are either not very discriminative or too complex. In this work, an effective and very simple plant recognition method is proposed. The main innovations of our method are threefold. (1) The feature maps of multiple pretrained...
Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-ind...
Despite significant advancements in remote sensing object tracking (RSOT) in recent years, achieving accurate and continuous tracking of tiny-sized targets remains a challenging task due to similar object interference and other related issues. In thi...
Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot...
This paper proposes DCGAN-YOLOv8n, an integrated framework that significantly advances small-sample target detection by synergizing generative adversarial feature enhancement with multi-scale representation learning. The model’s core contributi...
This paper proposes a novel approach to high-dynamic-range (HDR) imaging of dynamic scenes to eliminate ghosting artifacts in HDR images when in the presence of severe misalignment (large object or camera motion) in input low-dynamic-range (LDR) imag...
OpenMOC-HEX, a neutron transport calculation code with hexagonal modular ray tracing, has the capability of domain decomposition parallelism based on an MPI parallel programming model. In this paper, the optimization of inter-node communication was s...
Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing mul...
Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, whi...
Initiatives such as the United Nations Decade of Ocean Science for Sustainable Development and Seabed 2030 promote seabed mapping worldwide. In Brazil, especially on the Espírito Santo Continental Shelf, high-resolution seabed mapping has reve...
Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Netwo...
Background/Objectives: In patients with intracranial arteriovenous malformation (AVM), the first diagnostic analysis is often performed in emergency conditions by Computed Tomography (CT) and multiphase CT angiography (CTA). Nevertheless, once ruptur...
To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Sp...
The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for veget...
Fine-grained visual categorization (FGVC) presents significant challenges due to subtle inter-class variation and significant intra-class diversity, often leading to limited discriminative capacity in global representations. Existing methods inadequa...
Crack detection in cement infrastructure is imperative to ensure its structural integrity and public safety. However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is...
While existing tongue detection methods have achieved good accuracy, the problems of low detection speed and excessive noise in the background area still exist. To address these problems, a fast tongue detection model based on a lightweight model and...
In order to reduce the computational consumption of the training and the testing phases of video face recognition methods based on a global statistical method and a deep learning network, a novel video face verification algorithm based on a three-pat...
The goal of steel defect detection is to enhance the recognition accuracy and accelerate the detection speed with fewer parameters. However, challenges arise in steel sample detection due to issues such as feature ambiguity, low contrast, and similar...
For voice analysis, much work has been undertaken with a multitude of acoustic and electroglottographic metrics. However, few of these have proven to be robustly correlated with physical and physiological phenomena. In particular, all metrics are aff...
Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim-small-target detection suffers from complex background...
In this paper, we propose a novel loss function for deep face recognition, called the additive orthant loss (Orthant loss), which can be combined for softmax-based loss functions to improve the feature-discriminative capability. The Orthant loss make...
Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the bac...
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