Underwater optical images have outstanding advantages for short-range underwater target detection tasks. However, owing to the limitations of special underwater imaging environments, underwater images often have several problems, such as noise interf...
One important task for autonomous driving is the precise detection and recognition of road traffic signs. This research focuses on a comprehensive set of 72 distinct traffic signs that are prevalent on urban roads in China, with the goal of developin...
Side-scan sonar (SSS) images present unique challenges to computer vision due to their lower resolution, smaller targets, and fewer features. Although the mainstream backbone networks have shown promising results on traditional vision tasks, they uti...
Small object detection in remote sensing imagery is a fundamental task for visual information extraction, yet it remains challenging due to extremely small target scales, complex backgrounds, and the loss of discriminative feature information caused...
Building extraction by deep learning from remote sensing images is currently a research hotspot. PSPNet is one of the classic semantic segmentation models and is currently adopted by many applications. Moreover, PSPNet can use not only CNN-based netw...
Target detection in UAV aerial photography scenarios faces challenges of small targets and complex backgrounds. Thus, we proposed an improved YOLOv11n small-target detection algorithm. First, a detection head is added to the 160 × 160 resolutio...
To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion...
The recall rate of the original YOLOv4 model for detecting internal defects in aluminum alloy welds is relatively low. To address this issue, this paper introduces an enhanced model, YOLOv4-cs1. The improvements include optimizing the stacking method...
Extracting fine-grained features from person images has proven crucial in person re-identification (re-ID). Although the research of convolutional neural networks (CNN) has been very successful in person re-ID, due to the small receptive field and do...
Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. However, images captured by UAVs usually have multi-scale features, complex backgrounds, uneven illumination, an...
Accurate and efficient detection is pivotal for tiny objects in remote sensing. However, achieving a favorable accuracy-efficiency trade-off remains challenging due to the few informative pixels of small targets, frequent occlusions, cluttered backgr...
A novel YOLOv5 network is presented in this paper to quantify the degree of defects in continuously cast billets. The proposed network addresses the challenges posed by noise or dirty spots and different defect sizes in the images of these billets. T...
The easy availability and usability of photo-editing tools have increased the number of forgery attacks, primarily splicing attacks, thereby increasing cybercrimes. Because of an existing image-splicing tamper-detection algorithm based on deep learni...
Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditi...
Down-sampling-based video compression frameworks have shown great potential in improving compression efficiency in modern sensing and imaging systems. However, existing methods ignore critical spatial and temporal redundancy, and treat all frames uni...
To tackle the frequent missed and false detection issues arising from the tiny scale of objects and strong background clutter in UAV aerial photography scenarios, this paper proposes a novel algorithm named SODet-YOLO for UAV aerial imagery. First, t...
In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep...
Remote sensing target detection faces significant challenges in handling multi-scale targets, with the high similarity in color and shape between targets and backgrounds in complex scenes further complicating the detection task. To address this chall...
Recently, object detection has become a research hotspot in computer vision, which often detects regular images with small viewing angles. In order to obtain a field of view without blind spots, fisheye cameras, which have distortions and discontinui...
Many deep learning-based spatiotemporal fusion (STF) methods have been proven to achieve high accuracy and robustness. Due to the variable shapes and sizes of objects in remote sensing images, pyramid networks are generally introduced to extract mult...
Accurate target identification of UAV (Unmanned Aerial Vehicle)-captured images is a prerequisite for maritime rescue and maritime surveillance. However, UAV-captured images pose several challenges, such as complex maritime backgrounds, tiny targets,...
Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection proc...
Cloud detection constitutes a pivotal task in remote sensing preprocessing, yet detecting cloud boundaries and identifying thin clouds under complex scenarios remain formidable challenges. In response to this challenge, we designed a network model, n...
Semantic segmentation of high-resolution remote sensing images faces three major challenges in frequency-spatial feature fusion: background clutter mixed into high-frequency components, semantic discontinuities within large homogeneous regions, and l...
In response to problems such as large target scale variations, strong background noise, and blurred features leading by low contrast in infrared target detection in near space environments, this paper proposes an efficient detection model, YOLO-MARS,...
Lightweight detection methods are frequently utilized for unmanned system sensing; however, to tackle the challenge of low precision in detecting small targets on the water’s surface by unmanned surface vessels, we present an enhanced method fo...
Semantic segmentation of remote sensing images plays a crucial role in accurate land-cover classification and environmental monitoring. However, existing semantic segmentation networks still struggle with multiscale feature extraction and context mod...
Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an impo...
Transmission multispectral imaging (TMI) has potential value for medical applications, such as early screening for breast cancer. However, because biological tissue has strong scattering and absorption characteristics, the heterogeneity detection cap...
Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on...
To address the challenging task of precise segmentation in graphite mineral images, where large flake graphite, discrete fine particles, and embedded micro-particles coexist alongside strong background noise interference from surrounding rock, an imp...
Uncrewed Aerial Vehicles (UAVs) are instrumental in advancing the field of remote sensing. Nevertheless, the complexity of the background and the dense distribution of objects both present considerable challenges for object detection in UAV remote se...
The complexity of urban scenes presents a challenge for semantic segmentation models. Existing models are constrained by factors such as the scale, color, and shape of urban objects, which limit their ability to achieve more accurate segmentation res...
14 November 2026
Counting oilseed flax seedlings accurately is challenging because the plants are small, densely distributed, and often partially occluded against complex field backgrounds. We developed CWT-DEIM, a detector based on DEIM-HGNetv2-N, to detect and coun...
Infrared small target detection technology has been widely applied in the defense sector, including applications such as precision targeting, alert systems, and naval monitoring. However, due to the small size of their targets and the extended imagin...
In response to the problems of complex background, multi-scale dangerous goods and severe stacking in X-ray security images, this paper proposes a high-accuracy dangerous goods detection algorithm for X-ray security images based on the improvement of...
The rapid development of unmanned aerial vehicle (UAV) technology has contributed to the increasing sophistication of UAV-based object-detection systems, which are now extensively utilized in civilian and military sectors. However, object detection f...
Recently, deep learning-based methods have made great improvements in object detection in remote sensing images (RSIs). However, detecting tiny objects in low-resolution images is still challenging. The features of these objects are not distinguishab...
Infrared small-target detection plays a crucial role in applications such as public safety monitoring. However, it faces significant challenges due to the loss of target features, which weakens detection performance. To tackle this problem, this stud...
Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. Howev...
Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often priorit...
Oriented object detection in remote sensing images remains a challenging task due to arbitrary target rotations, extreme scale variations, and complex backgrounds. However, current rotated detectors still face several limitations: insufficient orient...
Unmanned aerial vehicle (UAV) imagery offers a promising alternative to manual and vehicle-based inspection for highway pavement distress detection, but the high-angle perspective reduces the relative size and feature richness of small distresses and...
Mixed-viewpoint pavement crack detection remains challenging because aerial (Drone) and ground-level (Ground) images exhibit substantially different feature distributions, while repeated downsampling inevitably weakens the representation of fine crac...
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing m...
In autonomous driving, detecting small and occluded objects remains a substantial challenge due to the complexity of real-world environments. To address this, we propose RSO-YOLO, an enhanced model based on YOLOv12. First, the bidirectional feature p...
The diameter of the optic nerve sheath is an important indicator for assessing the intracranial pressure in critically ill patients. The methods for measuring the optic nerve sheath diameter are generally divided into invasive and non-invasive method...
Generic object detection algorithms for natural images have been proven to have excellent performance. In this paper, fabric defect detection on optical image datasets is systematically studied. In contrast to generic datasets, defect images are mult...
In remote sensing imagery, objects smaller than
Accurate biological detection is crucial for autonomous navigation of underwater robots, yet severely challenged by optical degradation and scale variation in marine environments. While image enhancement and domain adaptation methods offer some mitig...
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