The extremely destructive nature of malware has become a major threat to Internet security. The research on malware detection techniques has been evolving. Deep learning-based malware detection methods have achieved good results by using large-scale,...
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resourc...
Peanut leaf disease detection in complex field environments faces two major challenges: distinguishing visually similar symptoms and identifying severely occluded lesions. This study presents YOLOv8-MSDH, a lightweight detection model built upon an i...
Real-time monitoring of ships is crucial for inland navigation management. Under complex conditions, it is difficult to balance accuracy, real-time performance, and practicality in ship detection and tracking. We propose a lightweight model, YOLOv8-F...
Automatic medical segmentation is crucial for assisting doctors in identifying disease regions effectively. As a state-of-the-art (SOTA) approach, generative AI models, particularly diffusion models, have surpassed GANs in generating high-quality ima...
As the most common safety hazard among road surface distresses, pavement cracks require fine-grained segmentation to support subsequent maintenance decision-making. However, existing methods find it difficult to cope with complex environmental interf...
This paper proposes a lightweight violent behavior recognition model, DualCascadeTSF-MobileNetV2, which is improved based on the temporal shift module and its subsequent research. By introducing the Dual Cascade Temporal Shift and Fusion module, the...
Agricultural pest detection through image analysis is a key technology in automated pest-monitoring systems. However, some existing pest detection models face excessive model complexity. This study proposes YOLO-LCE, a lightweight model based on the...
Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in...
In this paper, we propose a lightweight lithography machine learning-based hotspot detection model that integrates the Squeeze-and-Excitation (SE) attention mechanism and the Efficient Channel Attention (ECA) mechanism. These mechanisms can adaptivel...
Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame an...
Disease is one of the primary threats to maize growth. Currently, maize disease detection is mainly conducted in laboratories, making it difficult to promptly respond to diseases. To enable detection in the field, a lightweight model is required. The...
Aiming at the problems of insufficient vehicle detection accuracy, high misdetection and omission rate, and heavy model computational burden caused by complex lighting conditions, target occlusion, and other factors in urban traffic scenarios, this p...
This paper discusses the application of deep learning technology in recognizing vehicle black smoke in road traffic monitoring videos. The use of massive surveillance video data imposes higher demands on the real-time performance of vehicle black smo...
Named-Entity Recognition (NER) as a core task in Natural Language Processing (NLP) aims to automatically identify and classify specific types of entities from unstructured text. In recent years, the introduction of Transformer architecture and its de...
In exploring the flight delay problem, traditional deep learning algorithms suffer from low accuracy and extreme computational complexity; therefore, the deep flight delay prediction algorithm is difficult to directly deploy to the mobile terminal. I...
In natural agricultural environments, plant disease monitoring faces significant challenges, including a highly uneven (long-tail) distribution of disease species, tiny scales of early-stage lesions, and complex, variable backgrounds. These factors h...
Infected URLs are always regarded as a serious threat to cybersecurity, serving as pathways to phishing, maliciousness, and other offenses. Although transformer-based models have demonstrated good performance in malicious URL detection, their high co...
This paper proposes a lightweight DGNCA-Net insulator defect detection algorithm based on improvements to the YOLOv11 framework, addressing the issues of high computational complexity and low detection accuracy for small targets in machine vision-bas...
Metal corrosion in high-risk areas, such as high-altitude cables and chemical factories, is very complex and inaccessible to people, which can be a hazard and compromise people’s safety. Embedding deep learning models into edge computing device...
Gastrointestinal stromal tumors (GISTs), which usually develop with a significant malignant potential, are a serious challenge in stromal health. With Endoscopic ultrasound (EUS), GISTs can appear similar to other tumors. This study introduces a ligh...
To address the issue of the computational intensity and deployment difficulties associated with weed detection models, a lightweight target detection model for weeds based on YOLOv8s in maize fields was proposed in this study. Firstly, a lightweight...
In today’s world, electronics and networked systems, such as IoT devices, embedded platforms and smart environments, are increasingly popular and widespread. As a result, these systems become more exposed to cyber threats. The malicious URL is...
In order to study the mechanical properties and stress–strain relationship of geopolymer lightweight aggregate concrete (GLAC), 13 groups of different mix proportions were designed. The influence of the binder ratio, alkali–binder ratio,...
Using unmanned aerial vehicle (UAV) real-time remote sensing to monitor diseased plants or abnormal areas of orchards from a low altitude perspective can greatly improve the efficiency and response speed of the patrol in smart orchards. The purpose o...
As an important and nutrient-rich economic crop, apple is significantly threatened by leaf diseases, which severely impact yield, making the timely and accurate diagnosis and segmentation of these diseases crucial. Traditional segmentation models fac...
To meet the demands of the food industry for automatic sorting of block-shaped foods using DELTA robots, a machine vision detection method capable of quickly identifying such foods needs to be studied. This paper proposes a lightweight model that inc...
In the current paper, the dynamic mechanical properties of sustainable lightweight aggregate concrete (SLAC) were numerically studied with a newly developed mesoscopic model. In the model, a fissure-based filling method was utilized for placing spher...
Human organoids are 3D cell culture models that precisely replicate the microenvironment of real organs. In organoid-based experiments, assessing whether the internal vasculature has formed normally is essential for ensuring the reliability of experi...
The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Trad...
With the development of smart grids and home energy management systems, accurate load identification has become an important part of improving energy efficiency and ensuring electrical safety. However, traditional load identification methods struggle...
Massive wildfires have become more frequent, seriously threatening the Earth’s ecosystems and human societies. Recognizing smoke from forest fires is critical to extinguishing them at an early stage. However, edge devices have low computational...
Convolutional neural networks (CNNs) have transformed the landscape of image analysis and are widely applied across various fields. With their widespread adoption in fields like medical diagnosis and autonomous driving, CNNs have demonstrated powerfu...
Barnyard grass is a major noxious weed in paddy fields. Accurate and efficient identification of barnyard grass is crucial for precision field management. However, existing deep learning models generally suffer from high parameter counts and computat...
Mosquitoes of the genera Aedes and Culex are major vectors of mosquito-borne diseases, posing serious threats to public health. Accurate detection of these species is therefore crucial for disease prevention and vector control. Traditional identifica...
Sheep face recognition technology is critical in key areas such as individual sheep identification and behavior monitoring. Existing sheep face recognition models typically require high computational resources. When these models are deployed on mobil...
Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard comput...
A number of canisters need to be lightweight designed to store the spherical fuel elements (SFE) used in high-temperature gas-cooled reactors (HTGR). The main challenge for engineering is pursuing high-accuracy and high-efficiency optimization simult...
Agricultural fires significantly threaten global food systems, ecosystems, and rural economies, necessitating timely detection to prevent widespread damage. This study presents a lightweight and enhanced version of the YOLOv5s model, optimized for ea...
To address the challenges of rapid and accurate grape variety identification in natural orchard environments, along with the demand for efficient deployment on mobile devices, we propose in this paper YOLOv11n-GrapeLite, a lightweight model built upo...
Foamed lightweight concrete has been applied in different fields of civil engineering because of its superior properties, but the related research considering internal pore damage is limited. Based on statistical damage theory and considering the une...
Wearable sensors-based gait recognition is an effective method to recognize people’s identity by recognizing the unique way they walk. Recently, the adoption of deep learning networks for gait recognition has achieved significant performance improvem...
(1) With the development of artificial intelligence, people expect to use modern information technology to solve the critical problems encountered in agriculture. How to identify sunflower diseases as early and quickly as possible and take correspond...
SAR ship detection and recognition are important components of the application of SAR data interpretation, allowing for the continuous, reliable, and efficient monitoring of maritime ship targets, in view of the present situation of SAR interpretatio...
Fruit and vegetable inspection aids robotic harvesting in modern agricultural production. For rapid and accurate detection of fresh shiitake mushrooms, picking robots must overcome the complex conditions of the growing environment, diverse morphology...
Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Bes...
Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited computational capacity, which cannot meet the direct needs of autonomous vehicles for d...
The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time...
Human activity recognition in privacy-sensitive indoor environments requires sensing modalities that remain robust under illumination variation and background clutter while preserving user anonymity. To this end, this study proposes a lightweight rad...
Fe-Mn-Al-C lightweight steels have been of significant interest due to their excellent mechanical properties and unique microstructures. However, there has been limited focus on the dynamic deformation. Here, we systematically investigate the mechani...
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