Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in...
Object detection underpins safety-critical applications such as autonomous driving and intelligent surveillance, yet the inherent vulnerability of object detection models exposes them to the dual threat of adversarial and backdoor attacks. Existing d...
Unmanned aerial vehicle (UAV) object detection plays an essential role in modern visual perception, but it remains challenging because UAV imagery typically contains extremely small, densely distributed objects embedded in complex backgrounds. Conven...
With the rapid development of intelligent navigation technologies, accurate dynamic object detection in complex maritime environments remains a critical challenge due to occlusion, scale variation, and multi-target interference. To address these issu...
Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of t...
Unmanned Aerial Vehicle (UAV) image object detection holds significant application value in the low-altitude economy, traffic monitoring, intelligent agriculture, and disaster rescue. However, due to the top-down perspective, UAV images typically suf...
SLAM (Simultaneous Localization and Mapping), as one of the basic functions of mobile robots, has become a hot topic in the field of robotics this year. The majority of SLAM systems in use today, however, disregard the impact of dynamic objects on th...
Few-shot object detection (FSOD) remains a critical challenge in computer vision, where the limited training data significantly hinder model performance. Existing methods suffer from poor robustness and accuracy, primarily due to scale sparsity and i...
Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a static environment. However, these methods are incapabl...
RGB-D salient object detection (SOD) demonstrates its superiority in detecting in complex environments due to the additional depth information introduced in the data. Inevitably, an independent stream is introduced to extract features from depth imag...
Optical remote sensing object detection faces challenges such as large variations in scale, slender and direction-sensitive targets, complex backgrounds, and limited deployment resources. This paper proposes a lightweight geometrically decoupled stud...
The Poisson multi-Bernoulli mixture (PMBM) filter is an effective approach for multi-object tracking in complex scenarios. However, its performance deteriorates when surviving objects spawn, as the PMBM filter only classifies detected objects as eith...
Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) aims to localize visually dominant regions in large-scale remote sensing scenes for applications such as disaster monitoring and urban analysis. Visual saliency fundamentally arises fro...
Aerial object detection plays a critical role in numerous fields, utilizing the flexibility of airborne platforms to achieve real-time tasks. Combining visible and infrared sensors can overcome limitations under low-light conditions, enabling full-ti...
This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to...
This paper presents an experimental evaluation of a smart glasses-based cyber-physical system for the recognition of dangerous objects and the detection of dynamic life-threatening events. The proposed system integrates real-time computer vision and...
RGB-D salient object detection (RGB-D SOD) aims to accurately localize and segment visually salient objects by jointly leveraging RGB images and depth maps. Some existing methods rely on static fusion strategies with fixed paths and weights, which tr...
Achieving robust visual detection under challenging conditions such as poor illumination and target deformation remains a critical challenge for computer vision systems. Although the YOLO series of object detection algorithms excel in speed and accur...
Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications...
In the field of remote sensing (RS) object detection, efficient and accurate target recognition is crucial for applications such as national defense and maritime monitoring. However, existing detection methods either have high computational complexit...
The detection of arbitrary-oriented and multi-scale objects in satellite optical imagery is an important task in remote sensing and computer vision. Despite significant research efforts, such detection remains largely unsolved due to the diversity of...
Objects in aerial images often have arbitrary orientations and variable shapes and sizes. As a result, accurate and robust object detection in aerial images is a challenging problem. In this paper, an arbitrary-oriented object detection method for ae...
The deployment of object detection models on resource-constrained edge devices remains a substantial challenge, primarily because conventional static networks expend the same worst-case computational cost on every input, regardless of intrinsic diffi...
Weakly supervised object detection (WSOD) in remote sensing images (RSIs) has attracted more and more attention because its training merely relies on image-level category labels, which significantly reduces the cost of manual annotation. With the exp...
Power transmission line monitoring is critical for grid stability. However, existing object detection methods remain challenged by small-scale fittings in aerial transmission-line images because of feature sparsity, complex backgrounds, and computati...
Low-altitude unmanned-aerial-vehicle imagery exposes object detectors to a distinctive combination of tiny object footprints, dense instance layouts, abrupt scale variation, and weak texture under complex urban backgrounds. Existing detectors usually...
Fine-grained oriented object detection in remote sensing is challenged by extreme scale variation, arbitrary rotations, and dense layouts with structured clutter, where fixed feature selection and RoI extraction often lead to unstable localization an...
This paper proposes DyMC-YOLO, an efficient RGB–infrared object detector based on YOLOv13. The evaluated infrared modalities include thermal infrared in the M3FD benchmark and near-infrared in a self-constructed RGB–NIR grape dataset. To...
Cloud detection in satellite imagery plays a pivotal role in achieving high-accuracy retrieval of biophysical parameters and subsequent remote sensing applications. Although numerous methods have been developed and operationally deployed, their accur...
Ground-based detection of spaceborne dynamic objects, such as near-Earth asteroids and space debris, is essential for ensuring the safety of space operations. This paper presents YOLO-Dynamic, a novel detection algorithm aimed at addressing the limit...
The field of remote sensing target detection has experienced rapid development in recent years, demonstrating significant value in various applications. However, general detection algorithms still face many key challenges when dealing with directiona...
Compared with general images, objects in remote sensing (RS) images typically exhibit a conspicuous diversity due to their arbitrary orientations. However, many of the prevalent detectors generally apply an inflexible strategy in setting the angles o...
Detecting small targets poses significant challenges due to the limited feature information and the aggregation of features in deep feature maps. Existing single-stage detectors handle classification and regression separately, leading to inconsistent...
UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration...
In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation ac...
The real-time detection and localization of dynamic targets in cattle farms are crucial for the effective operation of intelligent equipment. To overcome the limitations of wearable devices, including high costs and operational stress, this paper pro...
Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing meth...
The autonomous movement of the mobile robotic system is a complex problem. If there are dynamic objects in the space when performing this task, the complexity of the solution increases. To avoid collisions, it is necessary to implement a suitable det...
Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current...
Moving object detection (MOD) plays an important role in many applications that aim to identify regions of interest in videos. However, most existing MOD methods ignore the variability brought by time-varying information. Additionally, many network f...
Current object detection algorithms perform inference on all samples at a fixed computational cost in the inference stage, which wastes computing resources and is not flexible. To solve this problem, a dynamic object detection algorithm based on a li...
The majority of modern object detectors rely on a set of pre-defined anchor boxes, which enhances detection performance dramatically. Nevertheless, the pre-defined anchor strategy suffers some drawbacks, especially the complex hyper-parameters of anc...
In autonomous driving scenarios, the presence of dynamic objects can significantly degrade the precision and recall of loop-closure detection. To further improve robustness, this paper proposes a loop-closure detection framework with dynamic-object s...
Object detection can accurately identify and locate targets in images, serving basic industries such as agricultural monitoring and urban planning. However, targets in remote sensing images have random rotation angles, which hinders the accuracy of r...
Object detection is an essential computer vision task that identifies and locates objects within images or videos and is crucial for applications such as autonomous driving, robotics, and augmented reality. Light Detection and Ranging (LiDAR) and cam...
Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep...
Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for...
Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems...
Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motio...
The growing use of remote-sensing technologies has placed greater demands on object-detection algorithms, which still face challenges. This study proposes a hierarchical adaptive feature aggregation network (HAF-YOLO) to improve detection precision i...
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