High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexi...
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...
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...
The mechanical properties of lightweight aggregate concrete (LWAC) depend on the mixing ratio of its binders, normal weight aggregate (NWA), and lightweight aggregate (LWA). To characterize the relation between various concrete components and the mec...
In response to the challenges of low detection efficiency, high omission rate in small target detection and high model complexity in wood surface defect detection, this study proposes a lightweight detection model based on YOLO, which integrates a du...
The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many...
A convolutional neural network (CNN) that was trained using datasets for multiple scenarios was proposed to facilitate real-time road semantic segmentation for various scenarios encountered in autonomous driving. However, the CNN inhibited the mutual...
Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details while uph...
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...
Recent climate change has brought extremely heavy rains and widescale flooding to many areas around the globe. However, previous flood prediction methods usually require a lot of computation to obtain the prediction results and impose a heavy burden...
Heart sounds play an important role in the initial screening of heart diseases. However, the accurate diagnosis with heart sound signals requires doctors to have many years of clinical experience and relevant professional knowledge. In this study, we...
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 the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network...
To address the limitations of insufficient feature representation, large model size, and high deployment cost in network traffic classification, a lightweight classification framework based on multi-teacher knowledge distillation is proposed. The fra...
This paper presents HERMEES—Holistic Evaluation and Ranking Model for Energy Efficient Systems. HERMEES is based on a multi-criteria decision-making (MCDM) model designed to select the optimal combination of lightweight cryptography (LWC) and t...
Recently, deep learning has been widely used in ship-radiated noise classification. To improve classification efficiency, avoiding high computational costs is an important research direction in ship-radiated noise classification. We propose a lightwe...
This study introduces an advanced ensemble methodology employing lightweight neural network models for identifying severe convective clouds from FY-4B geostationary meteorological satellite imagery. We have constructed a FY-4B based severe convective...
Performing object tracking tasks and efficiently perceiving the underwater environment in real time for underwater vehicles is a challenging task due to the complex nature of the underwater environment. A hybrid excitation model based lightweight Sia...
Currently, train component fault detection is predominantly carried out through manual inspection, a process that is inefficient, prone to high omission rates, and carries safety risks. This study proposes an innovative fault detection model for trai...
Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an ear...
Surface defect detection in industrial environments is crucial for quality management and has significant research value. General detection networks, such as the YOLO series, have proven effective in various dataset detections. However, due to the co...
Many heavy and lightweight convolutional neural networks (CNNs) require large datasets and parameter tuning. Moreover, they consume time and computer resources. A new lightweight model called FlexibleNet was created to overcome these obstacles. The n...
Narrowband Internet of Things (NB-IoT) was designed as a key Low-Power Wide-Area Network technology when 5G networks were established. The ideal quadrature demodulation in NB-IoT relies on the fundamental symmetry between the in-phase (I) and quadrat...
To ensure the safety and rational use of bridge traffic lines, the existing bridge structural damage detection models are not perfect for feature extraction and have difficulty meeting the practicability of detection equipment. Based on the YOLO (You...
Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually...
Currently, few deep models are applied to pepper-picking detection, and existing generalized neural networks face issues such as large model parameters, prolonged training times, and low accuracy. To address these challenges, this paper proposes the...
The generation of High-Dynamic-Range (HDR) images is essential for capturing details at various brightness levels, but current reconstruction methods, using deep learning techniques, often require significant computational resources, limiting their a...
Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an...
Efficient spectrum utilization is critical for meeting the growing data demands of modern wireless communication networks. Automatic Modulation Classification (AMC) plays a key role in enhancing spectrum efficiency by accurately identifying modulatio...
Remote sensing image (RSI) super-resolution plays a critical role in improving image details and reducing costs associated with physical imaging devices. However, existing super-resolution methods are not applicable to resource-constrained edge devic...
Smart cities have been envisioned to provide smartness in managing internet of things (IoT) application domains, such as transport and mobility, health care, natural resources, electricity and energy, homes and buildings, commerce and retail, society...
To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm wit...
In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These co...
As cyberattacks grow increasingly diverse and sophisticated, achieving accurate yet efficient network traffic anomaly detection has become a fundamental challenge in modern cybersecurity. While existing machine learning methods enable effective featu...
The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has...
Deep learning has significantly improved the quality of infrared and visible image fusion. However, existing mainstream deep fusion networks often come with complex architectures and a large number of parameters. While general lightweight techniques...
The model network based on YOLOv8 for detecting race cones and buckets in the Formula Unmanned Competition for Chinese university students needs help with problems with complex structure, redundant number of parameters, and computation, significantly...
Frequent and complex boarding operations require an effective management process for specialized tools. Traditional manual statistical analysis exhibits low efficiency, poor accuracy, and a lack of electronic records, making it difficult to meet the...
Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the e...
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...
Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical...
Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as se...
The fish detection algorithm is of great significance for obtaining aquaculture information, optimizing prey allocation in aquaculture, and improving the growth efficiency and survival rate of fish. To address the challenges of high complexity, large...
Numerous extant image dehazing methods based on learning improve performance by increasing the depth or width, the size of the convolution kernel, or using the Transformer structure. However, this will inevitably introduce many parameters and increas...
To address the demands of intelligent safflower harvesting scenarios, which require a safflower recognition model with both low computational cost and high detection performance, this paper proposes a lightweight improved model based on YOLOv11n, ter...
The traditional High-Resolution Range Profile (HRRP) target recognition method has difficulty automatically extracting target deep features, and has low recognition accuracy under low training samples. To solve these problems, a ship recognition meth...
Nowadays, lightweight aggregate concrete is becoming more popular due to its versatile properties. It mainly helps to reduce the dead loads of the structure, which ultimately reduces design load requirements. The main challenge associated with lightw...
Semantic segmentation for remote sensing images (RSIs) plays an important role in many applications, such as urban planning, environmental protection, agricultural valuation, and military reconnaissance. With the boom in remote sensing technology, nu...
In the digital twin system of mechatronics engineering, the scale and accuracy of models are continually improving. Nevertheless, this growth can hinder real-time interaction and decision-making accuracy within digital twins. The resulting delay impa...
Missing bolts compromise the structural integrity of bolted connections in steel bridges and industrial infrastructure. Manual visual inspection remains labour-intensive, subjective, and hazardous in hard-to-reach locations. This study presents a com...
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