Object-level saliency detection is an attractive research field which is useful for many content-based computer vision and remote-sensing tasks. This paper introduces an efficient unsupervised approach to salient object detection from the perspective...
Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this...
This study details the development of a lightweight and high performance model, targeting real-time object detection. Several designed features were integrated into the proposed framework to accomplish a light weight, rapid execution, and optimal per...
Object detection based on remote sensing imagery has become increasingly popular over the past few years. Unlike natural images taken by humans or surveillance cameras, the scale of remote sensing images is large, which requires the training and infe...
Detecting far-range cyclists from LiDAR point clouds is challenging because cyclists are small road users and are often represented by only a limited number of points at long distances. This study proposes a training-time object-level temporal merge...
Small-object detection in UAV imagery is difficult because targets occupy only a few pixels amid cluttered backgrounds. Although high-resolution features preserve fragile spatial cues, dense contextual interaction over these features is computational...
In recent years, pseudo point clouds generated from depth completion of RGB images and LiDAR data have provided a robust foundation for multimodal 3D object detection. However, the generation process often introduces noise, reducing data quality and...
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...
Object detection in unmanned aerial vehicle (UAV) imagery is severely challenged by extremely small object scales, cluttered backgrounds, and pronounced foreground–background imbalance, which jointly degrade the accuracy of general-purpose dete...
Accurate and efficient object detection in UAV images is a challenging task due to the diversity of target scales and the massive number of small targets. This study investigates the enhancement in the detection head using sparse convolution, demonst...
Object detection in aerial images has received extensive attention in recent years. The current mainstream anchor-based methods directly divide the training samples into positives and negatives according to the intersection-over-unit (IoU) of the pre...
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...
Object detection is a computer vision task of detecting instances of objects of a certain class, identifying types of objects, determining its location, and accurately labelling them in an input image or a video. The scope of the work presented withi...
Remote sensing small object detection remains a challenging task due to limited feature representation and interference from complex backgrounds. Existing methods that rely exclusively on either visible or infrared modalities often fail to achieve bo...
Detecting small objects in infrared images remains a challenge because most of them lack shape and texture. In this study, we proposed an infrared small-object detection method to improve the capacity for detecting thermal objects in complex scenario...
Multimodal fusion-based object detection is the foundational sensing task in scene understanding. It capitalizes on LiDAR and camera data to boost the robust results. However, there are still great challenges in establishing an effective fusion mecha...
Perception plays a crucial role in ensuring the safety and reliability of autonomous driving systems. However, the recognition and localization of small objects in complex scenarios still pose challenges. In this paper, we propose a point cloud objec...
In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure...
In recent times, there has been a notable surge in multimodal approaches that decorate raw LiDAR point clouds with camera-derived features to improve object detection performance. However, we found that these methods still grapple with the inherent s...
The Discriminative Correlation Filter (DCF) has been universally recognized in visual object tracking, thanks to its excellent accuracy and high speed. Nevertheless, these DCF-based trackers perform poorly in long-term tracking. The reasons include t...
Small object detection in UAV aerial imagery poses significant challenges due to sparse pixel representation and ambiguous object boundaries. Through systematic analysis, we identify three critical degradation stages during forward propagation in dee...
Deep learning methods have achieved remarkable success in remote sensing object detection. Existing object detection methods focus on integrating convolutional neural networks (CNNs) and Transformer networks to explore local and global representation...
Grid-based 3D object detection methods effectively leverage mature point cloud processing techniques and convolutional neural networks for feature extraction and object localization. However, unlike the 2D object detection domain, the unique characte...
This study addresses the challenges of sparse long-range point clouds, complex background interference, and inconsistent localization quality in 3D object detection for intelligent driving in open-pit mines. A camera–LiDAR multimodal detection...
Sparse radar point clouds provide limited evidence for three-dimensional (3D) object detection, making weak proposals especially sensitive to ranking and suppression. Standard post-processing depends on the primary detector’s scores and geometr...
The Generalized Hough Transform (GHT) is a powerful method for rigid shape detection under rotation, scaling, translation, and partial occlusion conditions, but its four-dimensional accumulator incurs prohibitive computational and memory demands that...
With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resourc...
LiDAR-based 3D object detection is essential for autonomous driving vehicles under poor lighting conditions. With LiDAR data, point cloud technologies have become increasingly important, as LiDAR sensors are largely cost down. However, the sparsity o...
In all-weather Earth observation and complex unstructured environments, traditional single-modal remote sensing object detection often fails due to low illumination and strong background interference. While RGB–infrared fusion provides compleme...
Onboard, real-time object detection in unmaned aerial vehicle remote sensing (UAV-RS) has always been a prominent challenge due to the higher image resolution required and the limited computing resources available. Due to the trade-off between accura...
Road-object detection for autonomous driving faces challenges including dense occlusions, large-scale variations, and imaging degradation under nighttime and adverse weather conditions, making it difficult for efficient detectors to balance accuracy...
Since trees are a vital part of urban green infrastructure, automatic mapping of individual urban trees is becoming increasingly important for city management and planning. Although deep-learning-based object detection networks are the state-of-the-a...
Printed circuit board (PCB) defect inspection is critical for ensuring product reliability, yet it remains challenging due to the microscopic scale of defects and complex background patterns. To improve the localization of fine anomalies, this paper...
Micro-crack detection on concrete surfaces is challenging because labeled micro-crack data are scarce, crack cues are extremely weak (often only a few pixels wide), and complex backgrounds (e.g., non-uniform illumination, shadows, and stains) degrade...
This paper presents a bidirectional feature fusion network (BiDFNet) for 3D object detection, leveraging pseudo-point clouds to achieve bidirectional fusion of point cloud and image features. The proposed model addresses key challenges in multimodal...
LiDAR point clouds often suffer from sparsity and uneven distributions in outdoor scenes, leading to the poor performance of cutting-edge 3D object detectors. In this paper, we propose Point-Rich, which is designed to improve the performance of 3D ob...
With the increasing complexity of neural network models, the huge communication overhead in federated learning (FL) has become a significant issue. To mitigate resource consumption, incorporating pruning algorithms into federated learning has emerged...
Robust bridge detection in ultra-high-resolution remote sensing imagery is important for transportation infrastructure mapping, geographic information system updating, and post-disaster assessment. However, local cropping can interrupt the spatial co...
Synthetic aperture radar (SAR) ship detection faces three persistent challenges: coherent speckle noise that obscures target boundaries, heterogeneous background clutter in coastal and harbor scenes, and ship targets whose spatial extent varies by mo...
Robust unmanned aerial vehicle (UAV) detection in real low-altitude anti-UAV scenarios remains challenging due to motion blur, extreme illumination, cluttered backgrounds, and tiny target sizes. Most existing UAV detectors rely on RGB imagery, but th...
Automated depalletizing systems aim to offer continuous and efficient operation in warehouse logistics, reducing cycle times and contributing to worker safety. However, most commercially available depalletizing solutions are designed primarily for hi...
The accuracy and real-time performance of 3D object detection are key factors limiting its widespread application. While cameras capture detailed color and texture features, they lack depth information compared to LiDAR. Multimodal detection combinin...
As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This st...
The fundamental challenge of salient object detection is to find the decision boundary that separates the salient object from the background. Low-rank recovery models address this challenge by decomposing an image or image feature-based matrix into a...
Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense...
In the age of deep learning, researchers have looked at domain adaptation under the pre-training and fine-tuning paradigm to leverage the gains in the natural image domain. These backbones and subsequent networks are designed for object detection in...
Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. “Sparse semantic” refers to the d...
A disease detection network based on a sparse parallel attention mechanism is proposed and experimentally validated in the passion fruit (Passiflora edulis [Sims]) disease detection task. Passiflora edulis, as a tropical and subtropical fruit tree, i...
In this paper, we propose a novel approach that enables simultaneous localization, mapping (SLAM) and objects recognition using visual sensors data in open environments that is capable to work on sparse data point clouds. In the proposed algorithm th...
Pixel-level depth information is crucial to many applications, such as autonomous driving, robotics navigation, 3D scene reconstruction, and augmented reality. However, depth information, which is usually acquired by sensors such as LiDAR, is sparse....
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