Computer vision applications require high-quality reproductions of original images, typically demanding complex models with many trainable parameters and floating-point operations. This increases computational load and restricts deployment on resourc...
Land-cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. We address the challenges in land-cover segmentation of remote-sensing imagery and complete the following work. Firstly...
This paper addresses the challenge of small-object detection in traffic surveillance by proposing a hybrid network architecture that combines attention mechanisms with convolutional layers. The network introduces an innovative attention mechanism int...
Image segmentation has played an essential role in computer vision. The target detection model represented by YOLOv5 is widely used in image segmentation. However, YOLOv5 has performance bottlenecks such as object scale variation, object occlusion, c...
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...
Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been prop...
This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping t...
One of the most effective image processing techniques is the use of convolutional neural networks that use convolutional layers. In each such layer, the value of the layer’s output signal at each point is a combination of the layer’s input signals co...
In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules...
In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semanti...
Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which i...
Herein, we proposed a multi-scale multi-band dilated time-frequency densely connected convolutional network (DenseNet) with long short-term memory (LSTM) for audio source separation. Because the spectrogram of the acoustic signal can be thought of as...
In the field of image denoising, convolutional neural networks (CNNs) have become increasingly popular due to their ability to learn effective feature representations from large amounts of data. In the field of image denoising, CNNs are widely used t...
The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we prop...
Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) to better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional...
The convolutional neural network (CNN) has been proven to have better performance in hyperspectral image (HSI) classification than traditional methods. Traditional CNN on hyperspectral image classification is used to pay more attention to spectral fe...
Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope i...
Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time per...
Convolutional neural networks (CNNs) have achieved great success in image classification tasks. In the process of a convolutional operation, a larger input area can capture more context information. Stacking several convolutional layers can enlarge t...
Crowd counting is an important task that serves as a preprocessing step in many applications. Despite obvious improvement reported by various convolutional-neural-network-based approaches, they only focus on the role of deep feature maps while neglec...
The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological fea...
The number of people in a crowd is crucial information in public safety, intelligent monitoring, traffic management, architectural design, and other fields. At present, the counting accuracy in public spaces remains compromised by some unavoidable si...
Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automa...
Image aesthetic assessment (IAA) is crucial for both scientific research and practical applications, and numerous studies have achieved promising performance. However, they still exhibit two major limitations: the neglect of hierarchical interactions...
Due to the complex nature of Chinese characters, junior international students often encounter writing problems related to strokes, components, and their combinations when writing Chinese characters. Digital ink Chinese characters (DICCs) are obtaine...
19 February 2020
The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehensio...
With the growing popularity of location-based social media applications, point-of-interest (POI) recommendation has become important in recent years. Several techniques, especially the collaborative filtering (CF), Markov chain (MC), and recurrent ne...
Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction b...
Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods....
Bearing fault diagnosis is an important task for condition monitoring and predictive maintenance of rotating machinery. Nevertheless, many existing deep learning-based methods have difficulty in jointly modeling multi-scale fault characteristics, ada...
In the manufacturing process of industrial robots, the defect detection of raw materials includes two types of tasks, which makes the defect detection guarantee its accuracy. It also makes the defect detection task challenging in practical work. In a...
Recently, many researchers have attempted to use convolutional neural networks (CNNs) for wildfire smoke detection. However, the application of CNNs in wildfire smoke detection still faces several issues, e.g., the high false-alarm rate of detection...
Medical image resolution plays a crucial role in early disease detection and fine-structure observation. Super-resolution reconstruction technology can restore low-resolution images to high-resolution versions, thereby assisting physicians in making...
In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addres...
The widespread use of the onion browser (Tor) has provided a breeding ground for the proliferation of cybercriminal activities and the Tor anonymous traffic identification method has been used to fingerprint anonymous web traffic and identify the web...
The faults of the landing gear retraction/extension(R/E) system can result in the deterioration of an aircraft’s maneuvering conditions; how to identify the faults of the landing gear R/E system has become a key issue for ensuring aircraft take-off a...
Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes....
Existing studies have shown that effective extraction of multi-scale information is a crucial factor directly related to the increase in performance of semantic segmentation. Accordingly, various methods for extracting multi-scale information have be...
To address the limitations of low modeling accuracy in physics-based methods—which often lead to poor vehicle-tracking performance and high energy consumption—this paper proposes an intelligent vehicle modeling and trajectory tracking con...
Effectively classifying each pixel in an image is an important research topic in semantic segmentation. The Existing methods typically require the network to directly generate a feature map of the same size as the original image and classify each pix...
A battery’s charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction...
With the rapid development of remote sensing technology, optical remote sensing images are increasingly being used in areas such as military reconnaissance, environmental monitoring, and urban planning. Due to the small number of pixels, fuzzy featur...
Arctic sea ice concentration plays a key role in the global ecosystem. However, accurate prediction of Arctic sea ice concentration remains a challenging task due to its inherent nonlinearity and complex spatiotemporal correlations. To address these...
Roads extracted from high-resolution remote sensing images are widely used in many fields, such as autonomous driving, road planning, disaster relief, etc. However, road extraction from high-resolution remote sensing images has certain deficiencies i...
During steel production, various defects often appear on the surface of the steel, such as cracks, pores, scars, and inclusions. These defects may seriously decrease steel quality or performance, so how to timely and accurately detect defects has gre...
Linear feature extraction is crucial for special objects in semantic segmentation networks, such as slot marking and lanes. The objects with linear characteristics have global contextual information dependency. It is very difficult to capture the com...
Underwater object detection is critical for marine ecological monitoring and biodiversity research, yet existing algorithms struggle in detecting densely packed objects of varying sizes, particularly in occluded and complex underwater environments. T...
The analysis and evaluation of landslide susceptibility are of great significance in preventing and managing geological hazards. Aiming at the problems of insufficient information caused by the limited number of landslide datasets, complex informatio...
Remote sensing images with high temporal and spatial resolutions play a crucial role in land surface-change monitoring, vegetation monitoring, and natural disaster mapping. However, existing technical conditions and cost constraints make it very diff...
Traffic police gesture recognition is important in automatic driving. Most existing traffic police gesture recognition methods extract pixel-level features from RGB images which are uninterpretable because of a lack of gesture skeleton features and m...
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