The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to erro...
Over the past few years, the study of environmental sound classification (ESC) has become very popular due to the intricate nature of environmental sounds. This paper reports our study on employing various acoustic features aggregation and data enhan...
Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It...
In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a comm...
Aggregate geometrical features directly affect asphalt pavement surface properties, which further affect the resistance to skidding of a road surface. In order to establish a relationship between the aggregate geometrical features and pavement surfac...
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...
The sustainable performance of asphalt pavement depends on the quality and mix design of road aggregates. Identifying aggregate morphology and size is a prerequisite step for material design and numerical modeling of asphalt mixtures. The paper aims...
Pansharpening methods based on deep learning can obtain high-quality, high-resolution multispectral images and are gradually becoming an active research topic. To combine deep learning and remote sensing domain knowledge more efficiently, we propose...
Feature selection is one of the key stages in the process of creating machine learning models and conducting data analysis. This paper presents the results of research related to the implementation of a new algorithm for feature selection and ranking...
3D object detection has recently become a research hotspot in the field of autonomous driving. Although great progress has been made, it still needs to be further improved. Therefore, this paper presents FDCA, a feature deep continuous aggregation ne...
Cloud detection constitutes a pivotal task in remote sensing preprocessing, yet detecting cloud boundaries and identifying thin clouds under complex scenarios remain formidable challenges. In response to this challenge, we designed a network model, n...
Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomp...
Monitoring raft aquaculture areas plays an important role in the sustainability of marine aquaculture. With the advantages of full-time observation and ability to penetrate clouds, synthetic aperture radar (SAR) imaging has replaced laborious on-site...
In this work, we focus on the misalignment problem in person re-identification. Human body parts commonly contain discriminative local representations relevant with identity recognition. However, the representations are easily affected by misalignmen...
This study presents a convolutional neural network (CNN)-based feature-scheduling aggregation clustering framework designed to address the limitations of conventional clustering algorithms in handling high-dimensional data structures. The proposed fr...
Remote sensing image scene classification (RSISC) has broad application prospects, but related challenges still exist and urgently need to be addressed. One of the most important challenges is how to learn a strong discriminative scene representation...
Strip steel surface defect detection remains a challenging task due to the diverse scales and uneven spatial distribution of defects, which often lead to incomplete feature representation and missed detections in sparsely distributed regions. To addr...
In the field of autonomous driving and robotics, 3D object detection is a difficult, but important task. To improve the accuracy of detection, LiDAR, which collects the 3D point cloud of a scene, is updated constantly. But the density of the collecte...
One of the most significant problems affecting a concrete bridge’s safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolution...
In the field of drone-based object tracking, utilization of the infrared modality can improve the robustness of the tracker in scenes with severe illumination change and occlusions and expand the applicable scene of the drone object tracking task. In...
In this paper, we propose an improved Region Proposal Network (RPN) by introducing a metric-based nonlinear classifier to compute the similarity between features extracted from the backbone network and those of new classes. This enhancement aims to i...
Colorectal cancer is one of the three most common cancers worldwide. Polyps are complex and have unclear boundaries, often leading to inaccurate boundary segmentation and missed detections. To address these challenges, we propose a boundary constrain...
For wafer surface defect detection, a new method based on improved Faster RCNN is proposed here to solve the problems of missing detection due to small objects and multiple boxes detection due to discontinuous objects. First, focusing on the problem...
In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep...
Classroom behavior can effectively reflect learning states, and thus classroom behavior detection is crucial for improving teaching methods and enhancing teaching quality. To address issues such as severe occlusions and large scale variations in stud...
Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal...
Maize is one of the world’s major food crops, and its yields are closely related to the sustenance of people. However, its cultivation is hampered by various diseases. Meanwhile, maize diseases are characterized by spots of varying and irregula...
One of the most important parts of a text-independent speaker verification system is speaker embedding generation. Previous studies demonstrated that shortcut connections-based multi-layer aggregation improves the representational power of a speaker...
Meta-path-based heterogeneous graph neural networks have received widespread attention for better mining the similarities between heterogeneous nodes and for discovering new recommendation rules. Most existing models depend solely on node IDs for lea...
In recent years, substantial research has focused on emotion recognition using multi-stream speech representations. In existing multi-stream speech emotion recognition (SER) approaches, effectively extracting and fusing speech features is crucial. To...
The sparse and uneven distribution of point clouds in LiDAR-captured outdoor scenes poses significant challenges for 3D object detection in autonomous driving. Specifically, the imbalance between foreground and background points can degrade detection...
In recent years, with the improvement of synthetic aperture radar (SAR) imaging resolution, it is urgent to develop methods with higher accuracy and faster speed for ship detection in high-resolution SAR images. Among all kinds of methods, deep-learn...
Small object detection in aerial images, particularly from Unmanned Aerial Vehicle (UAV) platforms, remains a significant challenge due to limited object resolution, dense scenes, and background interference. However, existing small object detectors...
Video object detection (VOD) is a sophisticated visual task. It is a consensus that is used to find effective supportive information from correlation frames to boost the performance of the model in VOD tasks. In this paper, we not only improve the me...
Accurately detecting tea bud leaves is crucial for the automation of tea picking robots. However, challenges arise due to tea stem occlusion and overlapping of buds and leaves, presenting varied shapes of one bud–one leaf targets in the field o...
In this work, we present a convolutional neural network (CNN) named CGFA-CNN for blind image quality assessment (BIQA). A unique two-stage strategy is utilized which firstly identifies the distortion type in an image using Sub-Network I and then quan...
The exponential increase in online reviews and recommendations makes document classification and sentiment analysis a hot topic in academic and industrial research. Traditional deep learning based document classification methods require the use of fu...
Recently, integrating convolutional neural networks (CNNs) with Mamba has shown notable advantages in hyperspectral image classification. However, existing Mamba–CNN hybrid frameworks typically adopt a parallel-branch architecture where identic...
In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture...
Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture o...
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for...
The shortage of natural aggregates has compelled the developers to devote their efforts to finding alternative aggregates. On the other hand, demolition waste from old constructions creates huge land acquisition problems and environmental pollution....
The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision i...
Accurate lesion segmentation in ultrasound images remains challenging because of severe speckle noise, low tissue contrast, ambiguous boundaries, and substantial variations in lesion morphology and scale. To address these limitations, we propose RDPA...
Original high-definition radar data contains rich environmental information, including distance, Doppler velocity, and azimuth. However, extracting robust features from such sparse and noisy frequency-domain data remains a challenge. To address this...
Community detection is a crucial technique for uncovering latent network structures, analyzing group behaviors, and understanding information dissemination pathways. Existing methods predominantly rely on static graph structural features, while negle...
The main challenges of semantic segmentation in vehicle-mounted scenes are object scale variation and trading off model accuracy and efficiency. Lightweight backbone networks for semantic segmentation usually extract single-scale features layer-by-la...
Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by v...
Cross-view geo-localization (CVGL) aims to match ground-level images with geo-tagged aerial imagery for precise localization, but remains challenging due to severe viewpoint discrepancies, partial correspondence, and significant domain shifts across...
With the rapid development of deep neural networks, salient object detection has achieved great success in natural images. However, detecting salient objects from optical remote sensing images still remains a challenging task due to the diversity of...
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