Aiming at the problems of glare interference, local overexposure and detail loss caused by artificial light sources such as vehicle lamps and street lamps in nighttime road scenes, as well as the challenges of existing glare suppression models with l...
This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapp...
Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality...
Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the...
Photovoltaic (PV) panel surface-defect detection technology is crucial for the PV industry to perform smart maintenance. Using computer vision technology to detect PV panel surface defects can ensure better accuracy while reducing the workload of tra...
Hyperspectral imaging has emerged as a novel imaging modality in the medical field, offering the ability to acquire images of biological tissues while simultaneously providing biochemical insights for in-depth tissue analysis. This approach facilitat...
To resolve the problems of deep convolutional neural network models with many parameters and high memory resource consumption, a lightweight network-based algorithm for building detection of Minnan folk light synthetic aperture radar (SAR) images is...
The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in...
Accurate and timely diagnosis of rice leaf diseases is crucial for smart agriculture leveraging vision sensors. However, existing lightweight convolutional neural networks (CNNs) often struggle in complex field environments, where small lesions, clut...
Monitoring water surface algal blooms from surveillance perspectives faces challenges such as small objects, low texture contrasts, dynamic background interferences, and limited labeled datasets. In this study, we propose GECA-YOLOv26, a lightweight...
Traditional deep learning models for printed circuit board (PCB) surface defect detection achieve high accuracy but incur heavy computational costs, making deployment on resource-constrained edge devices in industrial environments challenging. This s...
To accurately detect defects, we propose an enhanced model based on YOLOv8, named STE-YOLO. To address the aforementioned challenges, this paper adopts YOLOv8 as the improved model. The structure of this paper is as follows: We enhance the model&rsqu...
During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we pro...
The die bonding process significantly impacts the yield and quality of IC packaging, and its quality detection is also a critical image sensing technology. With the advancement of machine automation and increased operating speeds, the misclassificati...
The die bonding process is one of the most critical steps in the front-end semiconductor packaging process, as it significantly affects the yield of the entire IC packaging process. This research aims to find an efficient, intelligent vision detectio...
This paper proposes a lightweight gaze-tracking architecture named GhostBlock-Augmented Look to Coordinate Space (L2CS), which integrates GhostNet-based modules and depthwise separable convolution to achieve a better trade-off between model accuracy...
Current SAR ship detection methods face a critical trade-off between accuracy and computational efficiency, severely limiting their deployment on resource-constrained edge devices that are essential for distributed maritime surveillance systems. This...
Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new fr...
Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network...
Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. Nevertheless, succeeding techniques for detecting f...
COVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have the advantages of being low cost, non-invasive, and radiation fr...
Due to the limited computational resources of portable devices, target detection models for drone detection face challenges in real-time deployment. To enhance the detection efficiency of low, slow, and small unmanned aerial vehicles (UAVs), this stu...
Existing pavement defect detection models face challenges in balancing detection accuracy and speed while being constrained by large parameter sizes, hindering deployment on edge terminal devices with limited computing resources. To address these iss...
Forest fires pose significant hazards to ecological environments and economic society. The detection of forest fire smoke can provide crucial information for the suppression of early fires. Previous detection models based on deep learning have been l...
Cerebrovascular diseases, including stroke and intracranial aneurysm, affect millions worldwide and remain a leading cause of mortality and disability. Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) enables non-invasive visualization of intr...
Pig behavior statistics can reflect their health status. Conventional approaches depend on manual observation to derive behavioral information from video recordings, a process that demands substantial time and human effort. To overcome these limitati...
Maize faces a wide variety of pests and diseases, and its growing environment is complex, which makes detecting these problems quite challenging. Existing object detection algorithms usually require a lot of computing power, but edge computing device...
Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests....
Chip contour detection aims to detect damaged chips in chip slots during IC packaging and testing using vision facilities. However, the operation speed of the new chip transportation machine is too fast, and the current chip contour detection models,...
For reducing the parameters and computational complexity of networks while improving the classification accuracy of hyperspectral remote sensing images (HRSIs), a dynamic split pointwise convolution (DSPC) strategy is presented, and a lightweight con...
Small-vehicle detection in aerial imagery requires fine spatial detail within limited computational budgets. We propose YOLOv11n-Ghost-P2, which combines Ghost-based feature compression with a high-resolution P2 fusion branch. Relative to standard YO...
A vision-based autonomous driving system can usually fuse information about object detection and steering angle prediction for safe self-driving through real-time recognition of the environment around the car. If an autonomous driving system cannot r...
In cotton disease detection, the complex farmland environment and the varying scales of disease spots, especially the presence of small-target disease spots, limit the detection accuracy of lightweight models. To address this issue, an improved YOLOv...
Accurate high-throughput fruit detection is the core prerequisite for precision citrus management. Existing models face a critical trade-off between accuracy for small fruits and computational efficiency, restricting large-scale industry transformati...
Our previous work introduced the LW-YOLOv4-tiny and the LW-ResNet18 models by replacing traditional convolution with the Ghost Conv to achieve rapid object detection and steering angle prediction, respectively. However, the entire object detection an...
Despite the continuous development of convolutional neural networks, it remains a challenge to achieve performance improvement with fewer parameters and floating point operations (FLOPs) as a light-weight model. In particular, excessive expressive po...
Aiming to solve the problems of false detection, missed detection, and insufficient detection ability of infrared vehicle images, an infrared vehicle target detection algorithm based on the improved YOLOv5 is proposed. The article analyzes the image...
In response to the challenges posed by complex background textures and limited hardware resources in fabric defect detection, this study proposes a lightweight fabric defect detection algorithm based on an improved GSL-YOLOv8n model. Firstly, to redu...
In order to solve the problem of high computational resource consumption in fabric anomaly detection, we propose a lightweight network, GH-YOLOx, which integrates ghost convolutions and hierarchical GHNetV2 backbone together to capture both local and...
Accurate and efficient oriented detection is critical for remote sensing images, yet remains challenging due to multi-scale distribution, arbitrary orientations, and stringent computational constraints of onboard platforms. To mitigate these challeng...
Surface defect detection based on machine vision and convolutional neural networks (CNNs) is an important and necessary process that enables rubber ring manufacturers to improve production quality and efficiency. However, such automatic detection alw...
This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrati...
Balancing detection accuracy with model lightweightness remains a key challenge in remote sensing object detection. Although convolutional neural networks have improved performance, they typically require large-scale datasets, making few-shot detecti...
In order to solve the problem of large image noise and unremarkable features caused by factors such as fluctuations in the power of a light source during the terahertz image acquisition of wheat, this paper proposes a THz image-denoising model called...
In response to the issues of the existing sewer defect detection models, which are not applicable to small computing platforms due to their complex structure and large computational volume, as well as the low detection accuracy, a lightweight detecti...
Deploying accurate face recognition models on resource-constrained devices remains a significant challenge. Ghost modules mitigate feature-map redundancy by synthesizing half of each feature map through inexpensive linear transformations and form the...
With the development of aerospace technology, the variety and complexity of spacecraft components have increased. Traditional manual and machine learning-based detection methods struggle to accurately and quickly identify these parts. Deep learning-b...
Eggplant is an important vegetable crop, and accurate identification of fruit diseases is critical for ensuring yield and quality. However, field-captured eggplant images may contain small disease lesions, foliage occlusion, and illumination variatio...
In the previous work, we proposed LWGSE-YOLOv4-tiny and LWDSG-ResNet18, leveraging depthwise separable and Ghost Convolutions for fast self-driving control while achieving a detection speed of 24.9 FPS. However, the system fell short of Level 4 auton...
To address the pressing need for accurate and efficient detection of corn diseases, we propose a novel, lightweight object detection framework, CBS-YOLOv8 (C2f-BiFPN-SCConv YOLOv8), which builds upon the YOLOv8 architecture to enhance performance for...
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