To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs)...
Autonomous vehicles require in-depth knowledge of their surroundings, making path segmentation and object detection crucial for determining the feasible region for path planning. Uniform characteristics of a road portion can be denoted by segmentatio...
Due to the efficiency of self-attention mechanisms in encoding spatial information, Transformer-based models have recently taken a dominant position among semantic segmentation methods. However, Transformer-based models have the disadvantages of requ...
Intracranial aneurysms (IAs), a significant medical concern due to their prevalence and life-threatening nature, pose challenges regarding diagnosis owing to their diminutive and variable morphology. There are currently challenges surrounding automat...
Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access a...
Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes...
Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesio...
Reliable medical image segmentation remains challenging because models must preserve fine boundary details while maintaining global semantic consistency. CNNs capture local structures effectively but have limited long-range modeling ability, whereas...
Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective u...
This paper presents an integrated framework for automated detection of surface defects in infrastructures using unmanned aerial vehicles (UAVs), comprising 3D model-based adaptive path planning, high-resolution image acquisition, and an attention-enh...
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...
Abdominal CT images are important clues for diagnosing liver cancer lesions. However, liver cancer presents challenges such as significant differences in tumor size, shape, and location, which can affect segmentation accuracy. To address these challe...
In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet,...
During the process of autonomous tea harvesting, it is essential for the tea-harvesting robots to navigate along the tea canopy while obtaining real-time and precise information about these tea canopies. Considering that most tea gardens are located...
Over the past few years, researchers have demonstrated the possibilities to use the Computer-Aided Diagnosis (CAD) to provide a preliminary diagnosis. Recently, it is also becoming increasingly common for doctors and computer practitioners to collabo...
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...
Precise segmentation of the retinal microvasculature is vital for early detection and longitudinal monitoring of systemic cardiovascular and ophthalmic diseases. However, contemporary deep learning models struggle to simultaneously preserve fine-vess...
Wheat is one of the world’s essential crops, and the presence of foliar diseases significantly affects both the yield and quality of wheat. Accurate identification of wheat leaf diseases is crucial. However, traditional segmentation models face...
Landmine contamination is a significant problem that has devastating consequences worldwide. Unmanned aerial vehicles (UAVs) can play an important role in solving this problem. The technology has the potential to expedite, simplify, and improve the s...
Crash hotspot analysis has been widely studied in road traffic safety. Conventional approaches primarily rely on the spatial density or frequency of crash locations but fail to capture vehicle traversal patterns and segment-level exposure. In additio...
Benefiting from the booming of deep learning, the state-of-the-art models achieved great progress. But they are huge in terms of parameters and floating point operations, which makes it hard to apply them to real-time applications. In this paper, we...
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are common...
Diabetes can induce diseases including diabetic retinopathy, cataracts, glaucoma, etc. The blindness caused by these diseases is irreversible. Early analysis of retinal fundus images, including optic disc and optic cup detection and retinal blood ves...
According to China’s tourism statistics, the inbound tourism market is composed of eight types of travel motivations (sightseeing, leisure, business meeting (business-m), visiting relatives and friends (visiting-rf), shopping, religious worship...
Information-centric networks (ICNs) have received wide interest from researchers, and in-network caching is an important characteristic of ICN. The management and placement of contents are essential due to cache nodes’ limited cache space and t...
This paper proposes a Dual-path Feature-enhanced Fusion Network (DPF-Net) for medical image segmentation to address limitations in existing methods, including insufficient edge feature extraction, semantic gaps among multi-scale encoder features, and...
Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentati...
Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two chal...
Instance segmentation stands as a foundational technology in real-world applications such as autonomous driving, where the inherent trade-off between accuracy and computational efficiency remains a key barrier to practical deployment. To tackle this...
The application of drones has contributed to automated herbicide spraying in the context of precision agriculture. Although drone technology is mature, the widespread application of agricultural drones and their numerous advantages still demand impro...
Melanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper,...
The extraction of navigation lines plays a crucial role in the autonomous navigation of agricultural robots. This work offers a method of ridge navigation route extraction, based on deep learning, to address the issues of poor real-time performance a...
Deep learning methods based on convolutional neural networks (CNNs) and Mamba have advanced medical image segmentation, yet two challenges remain: (1) trade-off in feature extraction, where CNNs capture local details but miss global context, and Mamb...
Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing...
U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabi...
The study of the lead-lag relationship between the Hong Kong offshore Renminbi (CNH) spot market and onshore (CNY) spot market is of great importance for its wide application in market risk management. In this paper, we study the correlation between...
The automatic segmentation technique for colorectal polyps in colonoscopy is considered critical for aiding physicians in real-time lesion identification and minimizing diagnostic errors such as false positives and missed lesions. Despite significant...
The Hotan rose, a high-value specialty crop cultivated in Xinjiang, exhibits pronouncedly non-uniform flowering phenology, which has historically necessitated labor-intensive manual harvesting. To date, however, no integrated framework has been repor...
This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in...
Centerline tracking is useful in performing segmental analysis of vessel tortuosity in angiography data. However, a highly tortuous) artery can produce multiple centerlines due to over-segmentation of the artery, resulting in inaccurate path-finding...
Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small object...
Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and...
Background/Objectives: Virtual Bronchoscopic Navigation is used to guide bronchoscopes toward peripheral pulmonary lesions, but broad clinical and research adoption remains limited by the cost of proprietary software and by segmentation failures in s...
As a crucial computer vision task, multi-objective semantic segmentation has attracted widespread attention and research in the field of remote sensing image analysis. This technology has important application value in fields such as land resource su...
The discontinuities of a five-axis linear-segment toolpath result in fluctuation in the feedrate, acceleration and jerk commands that lead to machine tool vibration and poor surface finish. For path smoothing, with the global curve-fitting method it...
This paper deals with the automatic classification of customers on the basis of their movements around a sports shop center. We start by collecting coordinates from customers while they visit the store. Consequently, any costumer’s path through the s...
An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the propos...
Construction work zones are a difficult case for open-vocabulary semantic segmentation. Their layouts are temporary, safety-relevant objects that are often small and long-tailed, and generic models readily confuse them with background. We address the...
To address perception and navigation challenges in precision agriculture caused by GPS signal loss and weakly structured environments in greenhouses, this study proposes an integrated framework for real-time semantic reconstruction and path planning....
With the increasing demand for marine monitoring, the use of coverage path planning based on unmanned aerial vehicle (UAV) aerial images to assist multiple unmanned surface vehicles (USVs) has shown great potential in marine applications. However, ac...
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