38 Results Found

By virtue of the merits of wide swath, persistent observation, and rapid operational response, geostationary remote sensing satellites (e.g., GF-4) show tremendous potential for sea target system surveillance and situational awareness. However, ships...

Underwater survey and inspection are tasks of paramount relevance for a variety of applications. They are usually performed through the employment of optical and acoustic sensors installed aboard underwater vehicles, in order to capture details of th...

The human attention mechanism can be understood and simulated by closely associating the saliency prediction task to neuroscience and psychology. Furthermore, saliency prediction is widely used in computer vision and interdisciplinary subjects. In re...

Unsupervised change detection(CD) from remotely sensed images is a fundamental challenge when the ground truth for supervised learning is not easily available. Inspired by the visual attention mechanism and multi-level sensation capacity of human vis...

Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that...

The detection result of current light-field salient object detection methods suffers from loss of edge details, which significantly limits the performance of subsequent computer vision tasks. To solve this problem, we propose a novel convolutional ne...

In order to address driver’s dazzle caused by the abuse of high beams when vehicles meet at night, a night vision anti-halation algorithm based on image fusion combining visual saliency with YUV-FNSCT is proposed. Improved Frequency-turned (FT)...

The automatic detection of regions of interest (ROI) is useful for remote sensing image analysis, such as land cover classification, object recognition, image compression, and various computer vision related applications. Recently, approaches based o...

Salient object detection is a challenging task in the field of computer vision. The graph-based model has attracted lots of research attention and achieved remarkable progress in this task, which constructs graphs to formulate the intrinsic structure...

The detection of no-service rail surface defects is important in the rail manufacturing process. Detection of defects can prevent significant financial losses. However, the texture and form of the defects are often very similar to the background, whi...

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing w...

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual qualit...

The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variat...

Salient Object Detection (SOD) in RGB-D images plays a crucial role in the field of computer vision, with its central aim being to identify and segment the most visually striking objects within a scene. However, optimizing the fusion of multi-modal a...

This study proposes a unified deep-learning framework for the concurrent classification of Chronic Obstructive Pulmonary Disease (COPD) severity and regression of the FEV1/FVC ratio from chest X-ray (CXR) images. We integrated a ConvNeXt-Large backbo...

First responders operate in high-stakes environments, demanding rapid, accurate decision-making. Recent research has provided recommendations on how Augmented Reality (AR) could be utilized to support their efforts. Building off of these studies, thi...

Salient object detection aims to distinguish the most visually conspicuous regions, playing an important role in computer vision tasks. However, complex natural scenarios can challenge salient object detection, hindering accurate extraction of object...

Biological organisms perform complex selective attention operations continuously and effortlessly. These operations allow them to quickly determine the motor actions to take in response to combinations of external stimuli and internal states, and to...

The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements...

Transmission line fittings have been exposed to complex environments for a long time. Due to the interference of haze and other environmental factors, it is often difficult for the camera to obtain high quality on-site images, and the traditional ima...

The coating thickness of fuel particles is a critical parameter for ensuring the safe operation of high-temperature gas-cooled reactors. However, existing detection technologies still face limitations in measurement accuracy, efficiency, and automati...

Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer v...

Combining advanced gaze tracking systems with the latest vehicle environment sensors opens up new fields of applications for driver assistance. Gaze tracking enables researchers to determine the location of a fixation, and under consideration of the...

Infrared (IR) and visible (VIS) image fusion enhances vision tasks by combining complementary data. However, most existing methods assume normal lighting conditions and thus perform poorly in low-light environments, where VIS images often lose critic...

Background/Objectives: Despite the availability of screening services, the rate of diabetic retinopathy (DR) screening continues to be suboptimal in Australia, necessitating improvement. However, improving DR screening rates requires a more comprehen...

Pollen identification is critical for melissopalynology (honey authentication), ecological monitoring, and allergen tracking, yet manual microscopic analysis remains labor-intensive, subjective, and error-prone when multiple grains overlap in realist...

To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914...

Detecting small moving objects under challenging lighting conditions, such as overexposure and underexposure, remains a critical challenge in computer vision applications including surveillance, autonomous driving, and anti-UAV systems. Traditional R...

The aim of infrared and visible image fusion is to integrate the complementary information of the two modalities for high-quality fused images. However, many deep learning fusion algorithms have not considered the characteristics of infrared images i...

Automated damage segmentation for concrete bridges is a fundamental task in infrastructure maintenance, yet existing systems often depend heavily on large annotated datasets, which are costly and time-consuming to produce. This paper presents an inno...

Machine harvesting of cotton in Xinjiang has significantly improved harvesting efficiency; however, it has also resulted in a considerable increase in residual mulch content within the cotton, which has severely affected the quality and market value...

Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hi...

Semantic segmentation of ultra-high-resolution remote sensing (UHR-RS) imagery plays a critical role in land use and land cover analysis, yet it remains computationally intensive due to the enormous input size and high spatial complexity. Existing st...

Jiangnan water towns, as distinctive cultural landscapes in China, are confronting the dual challenge of surging tourist flows and imbalances in spatial distribution. Research on pedestrian dynamics has so far offered narrow coverage of influencing f...

With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. However, SOD in optical remote sensing images (ORSI-SOD) remains a challenging task due to the diversity of objec...

With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of compre...

Smoke detection technology based on computer vision is a popular research direction in fire detection. This technology is widely used in outdoor fire detection fields (e.g., forest fire detection). Smoke detection is often based on features such as c...

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to...