Small-scale rotor design must balance hover efficiency and acoustic performance; however, costly aeroacoustic evaluations make multi-objective optimization computationally demanding. This study develops a surrogate-assisted framework using eight radi...
Nowadays, remote sensing images are characterized by significant scale variations, a high density of small targets, and complex background conditions, which pose substantial challenges for small-object detection. To address these issues, we propose E...
The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neur...
The exploration of some dangerous or important small-scale magnetic objects requires accurate three-dimensional inversion results. In this paper, a three-dimensional inversion method for small-scale magnetic objects is proposed. Normalized magnetic s...
The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the uti...
Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers&...
Small object detection in real-world scenarios remains challenging due to two coupled factors: severe scale imbalance and progressive degradation of fine-grained cues during backbone downsampling. Under drastic scale variations, conventional detector...
Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of smal...
Small object detection in aerial images, particularly from Unmanned Aerial Vehicle (UAV) platforms, remains a significant challenge due to limited object resolution, dense scenes, and background interference. However, existing small object detectors...
In unmanned aerial vehicle (UAV)-based small object detection, enhancing object perception under complex backgrounds remains a critical challenge for current detection models. Owing to the small scale, low pixel occupancy, and cluttered backgrounds o...
Small objects in UAV imagery fail for two coupled reasons. Repeated downsampling destroys their evidence—
As one type of object detection, small object detection has been widely used in daily-life-related applications with many real-time requirements, such as autopilot and navigation. Although deep-learning-based object detection methods have achieved gr...
Small-object detection in unmanned aerial vehicle (UAV) imagery remains challenging because target objects often occupy only a few pixels, exhibit weak feature responses, and are easily obscured by complex backgrounds. These aspects significantly lim...
Small object detection in aerial imagery remains challenging due to sparse feature representation, limited spatial resolution, and complex background interference. Current deep learning approaches enhance detection performance through multi-scale fea...
Existing DEtection TRansformer-based (DETR) object detection methods have been widely applied to standard object detection tasks, but still face numerous challenges in detecting small objects. These methods frequently miss the fine details of small o...
Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility t...
Small-object detection in urban remote sensing imagery is essential for smart city applications, yet remains challenging due to limited target size, large scale variations, complex urban backgrounds, as well as the trade-off dilemma between detection...
Small object detection in UAV remote sensing remains challenging due to fine-grained feature loss during downsampling and unbalanced cross-scale feature aggregation. Conventional feature pyramid fusion may suffer from hard activation truncation or co...
Aiming at the low detection accuracy and poor positioning for small objects of single-stage object detection algorithms, we improve the backbone network of SSD (Single Shot MultiBox Detector) and present an improved SSD model based on multi-scale fea...
Land cover maps are indispensable for decision making, monitoring, and management in agricultural areas, but they are often only available after harvesting. To obtain a timely crop map of a small-scale arable landscape in the Swiss Plateau, we acquir...
Detecting small objects in unmanned aerial vehicle (UAV) imagery remains a challenging task due to the limited target scale, cluttered backgrounds, severe occlusion, and motion blur commonly observed in dynamic aerial environments. This study present...
Semantic labeling of high-resolution remote sensing images (HRRSIs) holds a significant position in the remote sensing domain. Although numerous deep-learning-based segmentation models have enhanced segmentation precision, their complexity leads to a...
Recent YOLO-based object detection methods have demonstrated strong performance in real-time applications due to their efficient end-to-end architecture. However, in complex scenarios such as VisDrone2019, existing methods still face limitations in s...
Small object detection in unmanned aerial vehicle (UAV) imagery poses significant challenges due to insufficient feature representation, complex background interference, and extremely small target sizes. These factors collectively degrade the perform...
Satellite remote sensing has become a vital data source for monitoring power infrastructure. As critical infrastructure supporting power line operations, the monitoring and maintenance of power towers have become core elements in ensuring grid reliab...
Small object detection (SOD) in remote sensing images (RSIs) is a challenging task due to scale variation, severe occlusion, and complex backgrounds, often leading to high miss and false detection rates. To address these issues, this paper proposes a...
Accurate and efficient object detection is essential for aerial airport surveillance, playing a critical role in aviation safety and the advancement of autonomous operations. Although recent deep learning approaches have achieved notable progress, si...
Small objects in underwater and traffic-sign images often have weak boundaries and low contrast. Downsampling and multi-scale fusion can further weaken the features needed to detect them. We propose ContextFlow-YOLO, a YOLOv8n-based detector designed...
In remote sensing imagery, objects smaller than
Small object detection is fundamentally constrained by the lack of discriminative fine-grained features. Although introducing higher resolution detection scales can improve performance, it also amplifies background noise. In addition, the independent...
Small object detection in UAV remote sensing imagery has long faced significant challenges. Existing Transformer-based detectors still suffer from feature degradation and insufficient multi-scale information fusion when handling small objects with sp...
Small-object detection in unmanned aerial vehicle (UAV) remote sensing imagery is challenged by dense target distributions, substantial scale variation, complex ground backgrounds, and limited edge-computing resources. To address these challenges, we...
In response to the problem of poor detection ability of object detection models for small-scale targets in intelligent transportation scenarios, a fusion method is proposed to enhance the features of small-scale targets, starting from feature utiliza...
The intrusion of foreign objects on airport runways during aircraft takeoff and landing poses a significant safety threat to air transportation. Small-scale Foreign Object Debris (FOD) cannot be ruled out on time by traditional manual inspection, and...
In protected agriculture such as greenhouses, pest and disease monitoring via UAVs and fixed cameras suffers from extremely small object proportions owing to shooting altitude constraints, posing considerable detection challenges. Moreover, fine-grai...
As autonomous-driving scenarios become increasingly complex, object detection in road environments remains challenging, especially for small-scale, visually ambiguous, and partially occluded targets. These difficulties are closely related to the loss...
Remote sensing image change detection (RS-CD) has made impressive progress with the help of deep learning techniques. Small object change detection (SoCD) still faces many challenges. On the one hand, when the scale of changing objects varies greatly...
Small object detection in traffic sign applications often faces challenges like complex backgrounds, blurry samples, and multi-scale variations. Existing solutions tend to complicate the algorithms. In this study, we designed an efficient and simple...
In recent years, object detection using aerial images captured by unmanned aerial vehicles (UAVs) has become a research hotspot. However, due to the high resolution of UAV imagery, large variations in object scales, and the predominance of small targ...
To address the problems of severe fruit occlusion, large variations in target scale, and many small-scale goals being overlooked in the recognition of trellised watermelons under complex agricultural scenarios, this study proposes an improved RT-DETR...
Low-altitude UAVs pose increasing security concerns due to their small sizes, high mobility, and low observability. Detecting such targets in cluttered environments remains challenging due to strong background interference and significant scale varia...
Detecting small objects and objects with large scale variants are always challenging for deep learning based object detection approaches. Many efforts have been made to solve these problems such as adopting more effective network structures, image fe...
Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a cons...
The performance of deep learning-based object detection methods is heavily dependent on the characteristics of training data. However, real-world detection datasets often exhibit severe imbalance in object-scale distributions, resulting in insufficie...
Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of th...
In the intelligent recognition of construction drawings, architectural components significantly vary in size, and small objects are easily overlooked. To address these two challenges, this paper proposes a multi-scale feature fusion method based on c...
Small object detection in Unmanned Aerial Vehicles’ (UAVs) aerial images faces challenges such as low detection accuracy and complex backgrounds. Meanwhile, it is difficult to deploy the object detection models with large parameters on resource...
Unmanned aerial vehicles (UAVs) have been widely used in aerial photography and target detection tasks due to their flexibility and unique perspective. However, small targets often suffer from insufficient resolution, uneven scale distribution, and c...
Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and...
The increasing global demand for energy-efficient cooling systems, combined with the need to reduce greenhouse gas emissions, has led to growing interest in using low-GWP (global warming potential) refrigerants. This study conducts a multi-objective...
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