Papers Small Object Detection
“Small Object Detection” 태그가 달린 논문 203편 · 필터 해제
ScopeMamba-YOLO: Widening the Perceptual Scope Inward and Outward for Small Object Detection in Remote Sensing Imagery
Small object detection in unmanned aerial vehicle (UAV) and remote sensing imagery requires preserving high-resolution detail while modeling long-range context. Adding a stride-4 detection level and removing the stride-3…
Small Object DetectionMetric-Guided Synthetic Image Data Rendering for Deep Learning compatible with Agentic AI
Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone process. Synthetic data generation through 3D modelling and rendering …
Synthetic Data GenerationSmall Object DetectionFSDC-DETR: A Frequency-Spatial Domain Collaborative DETR for Small Object Detection
Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by rigid processing that entangle spatial aggregation with implicit frequency…
Small Object DetectionFRFDet: Efficient UAV Small Object Detection with Symmetric Sampling and Scalable Fusion
Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe backgr…
Small Object DetectionAdaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection
Small Object Detection (SOD) is a fundamental yet challenging problem in computer vision due to its limited spatial resolution and weak visual cues. Although recent approaches have achieved remarkable advances, the backg…
Small Object DetectionFrom Spatial to Spectral: An Efficient, Frequency-Guided Feature Representation Learner for Small Object Detection
Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequen…
Small Object DetectionFollowing the Flow: Advection-Consistent Modeling for Event-based Small Object Detection
Event cameras enable high-frequency visual perception with microsecond latency, offering advantages for dynamic scenes. However, event-based small object detection remains challenging due to sparse asynchronous measureme…
Computational EfficiencySmall Object DetectionContext-Aware Feature-Fusion for Co-occurring Object Detection in Autonomous Driving
Object detection in autonomous driving requires precise localization and an inherent understanding of the relational context between co-occurring objects. In extremely complex heterogeneous environments rare classes, sma…
Small Object DetectionAutonomous DrivingIntra-YOLO: A Small Object Detection Model for Caries and Molar-Incisor Hypomineralization in Intraoral Photography Based on Transfer Learning with Reinforcement Learning
This study developed a computer-aided diagnosis (CAD) system for detecting caries and molar-incisor hypomineralization (MIH) in intraoral photographs. These lesions share similar appearances, making clinical differentiat…
Reinforcement LearningSmall Object DetectionTransfer LearningSmall Object Detection in Industrial Recycling: A New Dataset and YOLO Performance Evaluation
In this paper, we address the problem of detecting small, dense, and overlapping objects, a major challenge in computer vision. Our focus is on reviewing proposed methods based on deep learning supervised approaches. We …
Computational EfficiencySmall Object DetectionData AugmentationAnomaly DetectionUHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery
Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small objects in such vast scenes presents a critical dilemma: retaining the…
Small Object DetectionAdaptive Slicing-Assisted Hyper Inference for Enhanced Small Object Detection in High-Resolution Imagery
Deep learning-based object detectors have achieved remarkable success across numerous computer vision applications, yet they continue to struggle with small object detection in high-resolution aerial and satellite imager…
Small Object DetectionFSDETR: Frequency-Spatial Feature Enhancement for Small Object Detection
Small object detection remains a significant challenge due to feature degradation from downsampling, mutual occlusion in dense clusters, and complex background interference. To address these issues, this paper proposes F…
Small Object DetectionGeneralized Small Object Detection:A Point-Prompted Paradigm and Benchmark
Small object detection (SOD) remains challenging due to extremely limited pixels and ambiguous object boundaries. These characteristics lead to challenging annotation, limited availability of large-scale high-quality dat…
Small Object DetectionCollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detection
Small object detection in unmanned aerial vehicle (UAV) imagery is challenging because high-altitude viewpoints produce severe scale variation, weak structural cues, and tight computational budgets. Existing lightweight …
Small Object DetectionSmall Object Detection in Complex Backgrounds with Multi-Scale Attention and Global Relation Modeling
Small object detection under complex backgrounds remains a challenging task due to severe feature degradation, weak semantic representation, and inaccurate localization caused by downsampling operations and background in…
Small Object DetectionWhen Bigger is Worse: A Practitioner's Guide to Model Selection Under Data Scarcity
Scaling laws assume larger models trained on more data consistently outperform smaller ones -- an assumption that drives model selection in computer vision but remains untested in resource-constrained Earth observation (…
Small Object DetectionSmall Object Detection Model with Spatial Laplacian Pyramid Attention and Multi-Scale Features Enhancement in Aerial Images
Detecting objects in aerial images confronts some significant challenges, including small size, dense and non-uniform distribution of objects over high-resolution images, which makes detection inefficient. Thus, in this …
Small Object DetectionLAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagery
Unmanned aerial vehicles serve as primary sensing platforms for surveillance, traffic monitoring, and disaster response, making aerial object detection a central problem in applied computer vision. Current detectors stru…
Small Object DetectionEFSI-DETR: Efficient Frequency-Semantic Integration for Real-Time Small Object Detection in UAV Imagery
Real-time small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to limited feature representation and ineffective multi-scale fusion. Existing methods underutilize frequency information …
Small Object Detection