Biological recognition methods often use biological characteristics such as the human face, iris, fingerprint, and palm print; however, such images often become blurred under the limitation of the complex environment of the underground, which leads t...
In this paper, we propose a new architecture of densely connected convolutional networks for pan-sharpening (DCCNP). Since the traditional convolution neural network (CNN) has difficulty handling the lack of a training sample set in the field of remo...
Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that t...
Skin lesion classification is an effective approach aided by computer vision for the diagnosis of skin cancer. Though deep learning models presented advantages over traditional methods and brought tremendous breakthroughs, a precise diagnosis is stil...
At present, ResNet and DenseNet have achieved significant performance gains in the field of finger-vein biometric recognition, which is partially attributed to the dominant design of cross-layer skip connection. In this manner, features from multiple...
The automatic segmentation of skin lesions is considered to be a key step in the diagnosis and treatment of skin lesions, which is essential to improve the survival rate of patients. However, due to the low contrast, the texture and boundary are diff...
Airborne speech enhancement is always a major challenge for the security of airborne systems. Recently, multi-objective learning technology has become one of the mainstream methods of monaural speech enhancement. In this paper, we propose a novel mul...
Motor imagery (MI) is a classical method of brain–computer interaction (BCI), in which electroencephalogram (EEG) signal features evoked by imaginary body movements are recognized, and relevant information is extracted. Recently, various deep-learnin...
Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs...
Fault diagnosis in axial piston pumps is key to ensuring the proper operation of a hydraulic system. Slipper wear, as a typical fault in piston pumps, is challenging to accurately diagnose because the faults are very similar for different forms and d...
Pulmonary nodule detection in chest computed tomography (CT) is of great significance for the early diagnosis of lung cancer. Therefore, it has attracted more and more researchers to propose various computer-assisted pulmonary nodule detection method...
The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in...
Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage...
In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a den...
Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image...
Diabetes has recently emerged as a worldwide problem, and diabetic retinopathy is an abnormal state associated with the human retina. Due to the increase in daily screen-related activities of modern human beings, diabetic retinopathy is more prevalen...
Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and improve accura...
Seabed sediment classification is of great significance in acoustic remote sensing. To accurately classify seabed sediments, big data are needed to train the classifier. However, acquiring seabed sediment information is expensive and time-consuming,...
In visual speech recognition (VSR), speech is transcribed using only visual information to interpret tongue and teeth movements. Recently, deep learning has shown outstanding performance in VSR, with accuracy exceeding that of lipreaders on benchmark...
In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) image fusion (MS pansharpening). However, the small-scale data and the gradient vanishing problem have be...
Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-re...
The accurate acquisition of water information from remote sensing images has become important in water resources monitoring and protections, and flooding disaster assessment. However, there are significant limitations in the traditionally used index...
We develop a deep learning-based matching method between an RGB (red, green and blue) and an infrared image that were captured from satellite sensors. The method includes a convolutional neural network (CNN) that compares the RGB and infrared image p...
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractab...
The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environme...
The Epiretinal Membrane (ERM) is an ocular disease that appears as a fibro-cellular layer of tissue over the retina, specifically, over the Inner Limiting Membrane (ILM). It causes vision blurring and distortion, and its presence can be indicative of...
Meltblown nonwoven fabrics are used in various products, such as masks, protective clothing, industrial filters, and sanitary products. As the range of products incorporating meltblown nonwoven fabrics has recently expanded, numerous studies have bee...
Convolutional neural network (CNN) has shown excellent performance in hyperspectral image (HSI) classification. However, the structure of the CNN models is complex, requiring many training parameters and floating-point operations (FLOPs). This is oft...
The safety of chemical processes is of critical importance. However, traditional fault monitoring methods have insufficiently studied the monitoring accuracy of multi-channel data and have not adequately considered the impact of noise on industrial p...
Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a c...
To meet the need for multispectral images having high spatial resolution in practical applications, we propose a dense encoder–decoder network with feedback connections for pan-sharpening. Our network consists of four parts. The first part consists o...
Breast cancer is the most common female cancer worldwide, and breast cancer accounts for 30% of female cancers. Of all the treatment modalities, breast cancer survivors who have undergone chemotherapy might complain about cognitive impairment during...
Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this p...
Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discov...
The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many...
Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. I...
This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in co...
With the thriving of deep learning, 3D convolutional neural networks have become a popular choice in volumetric image analysis due to their impressive 3D context mining ability. However, the 3D convolutional kernels will introduce a significant incre...
Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseN...
Accurate vehicle type classification plays a significant role in intelligent transportation systems. It is critical to understand the road conditions and usually contributive for the traffic light control system to respond correspondingly to alleviat...
In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems,...
Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on co...
In this paper, we examine and research the effect of long skip connection on convolutional neural networks (CNNs) for the tasks of image (surface defect) classification. The standard popular models only apply short skip connection inside blocks (laye...
Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision tech...
The development of display technology has continuously increased the requirements for image resolution. However, the imaging systems of many cameras are limited by their physical conditions, and the image resolution is often restrictive. Recently, se...
Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization pro...
To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map informatio...
In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area o...
To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic...
Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking. This study...
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