MatMMExtract pipeline creates MatSciFig dataset of 391k annotated materials science figure panels and MaterialScope detection dataset with high accuracy.
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Yolov10: Real-time end-to-end object detection
28 Pith papers cite this work, alongside 1,045 external citations. Polarity classification is still indexing.
28
Pith papers citing it
1,045
external citations · Pith
abstract
Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance.
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representative citing papers
M²E-UAV is the first benchmark dataset and evaluation protocol for tiny UAV detection from a moving event camera in motion-on-motion conditions.
DHNet with patch alignment and dual hypergraph fusion reaches SOTA RGBT video object detection on VT-VOD50 and the new large-scale DVT-VOD1000 benchmark.
Presents MMIO benchmark and RTVP method achieving state-of-the-art 42.2% AP in zero-shot industrial defect detection.
RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
Releases a recycling-specific dataset of >10k images and evaluates YOLO variants on small dense overlapping objects with augmentation and anomaly detection.
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
A global Sentinel-1 SAR time series dataset of 14.8M 1D backscatter profiles at 15,606 offshore wind infrastructure locations with a rule-based event classifier (macro F1=0.84) and a 553-series expert-annotated benchmark.
SoftHGNN introduces differentiable soft hyperedges via learnable prototypes and top-k sparse selection to model high-order visual interactions and improve recognition accuracy.
YOLOv12 is a new attention-based real-time object detector that reports higher accuracy than YOLOv10, YOLOv11, and RT-DETR variants at comparable or better speed and efficiency.
PNAFusion proposes pixel-neighborhood cross-attention and adaptive deformable alignment integrated progressively to boost efficiency and accuracy in multispectral object detection.
TinyFormer adds Parallel Bi-fusion Module and Spatial Semantic Adapter to a YOLO-DETR hybrid, raising small-object AP by 1.6 points to 58.5% on MS COCO while keeping real-time speed.
STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.
Vision-aided deep learning delivers 98.96% beam prediction accuracy and over 98% proactive blockage prediction for mm-wave links, including the first treatment of simultaneous non-uniform mobility.
A scale-robust lightweight CNN for glottis segmentation achieves 92.9% mDice at over 170 FPS with a 19 MB model size on three datasets.
A unified pipeline using OCR, inpainting, and diffusion models restores text in degraded documents on a new synthetic benchmark dataset, evaluated with the proposed UCSM metric.
Introduces UAVDB dataset for UAV detection/segmentation via PIC point-to-box conversion and SAM2 masks, with YOLO baselines showing PIC+SAM2 outperforms prior annotation methods on IoU.
A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.
A hierarchically decoupled heterogeneous MoE framework with YOLO experts and lightweight gating network reports 76.8% mAP50-95 on a composite traffic sign dataset, a 2.3% gain over baseline with 39.4% lower compute.
Proposes a knowledge-adaptive edge expert agent architecture for sustainable biodiversity monitoring that separates visual perception from reasoning with an explicit knowledge base.
DFIR-DETR augments RT-DETR with frequency-domain iterative refinement and dynamic feature aggregation, reporting 92.9% mAP50 on NEU-DET and 51.6% on VisDrone at 11.7M parameters and 47.2 GFLOPs.
MinerU delivers an open-source pipeline for high-precision document content extraction by integrating specialized models with tuned preprocessing and postprocessing rules.
A multi-objective optimization framework is proposed to assess KPIs in goal-oriented IoT service deployment, with simulation results indicating network efficiency benefits.
YOLO11n achieves the highest mAP@0.5:0.95 of 0.6065 for apple localization, with other detectors showing trade-offs in recall and precision at low confidence thresholds.
citing papers explorer
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Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature
MatMMExtract pipeline creates MatSciFig dataset of 391k annotated materials science figure panels and MaterialScope detection dataset with high accuracy.
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M$^2$E-UAV: A Benchmark and Analysis for Onboard Motion-on-Motion Event-Based Tiny UAV Detection
M²E-UAV is the first benchmark dataset and evaluation protocol for tiny UAV detection from a moving event camera in motion-on-motion conditions.
-
Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark
DHNet with patch alignment and dual hypergraph fusion reaches SOTA RGBT video object detection on VT-VOD50 and the new large-scale DVT-VOD1000 benchmark.
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Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline
Presents MMIO benchmark and RTVP method achieving state-of-the-art 42.2% AP in zero-shot industrial defect detection.
-
RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection
RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
-
Small Object Detection in Industrial Recycling: A New Dataset and YOLO Performance Evaluation
Releases a recycling-specific dataset of >10k images and evaluates YOLO variants on small dense overlapping objects with augmentation and anomaly detection.
-
BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation
BabelDOC uses an intermediate representation to decouple layout from content for improved layout-preserving PDF translation.
-
Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series
A global Sentinel-1 SAR time series dataset of 14.8M 1D backscatter profiles at 15,606 offshore wind infrastructure locations with a rule-based event classifier (macro F1=0.84) and a 553-series expert-annotated benchmark.
-
SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition
SoftHGNN introduces differentiable soft hyperedges via learnable prototypes and top-k sparse selection to model high-order visual interactions and improve recognition accuracy.
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YOLOv12: Attention-Centric Real-Time Object Detectors
YOLOv12 is a new attention-based real-time object detector that reports higher accuracy than YOLOv10, YOLOv11, and RT-DETR variants at comparable or better speed and efficiency.
-
Progressive Pixel-Neighborhood Deformable Cross-Attention for Multispectral Object Detection
PNAFusion proposes pixel-neighborhood cross-attention and adaptive deformable alignment integrated progressively to boost efficiency and accuracy in multispectral object detection.
-
TinyFormer: Preserving Tiny Objects in YOLO-DETR Hybrid Real-time Detectors
TinyFormer adds Parallel Bi-fusion Module and Spatial Semantic Adapter to a YOLO-DETR hybrid, raising small-object AP by 1.6 points to 58.5% on MS COCO while keeping real-time speed.
-
STAR-IOD: Scale-decoupled Topology Alignment with Pseudo-label Refinement for Remote Sensing Incremental Object Detection
STAR-IOD applies scale-decoupled topology alignment and K-Means-based pseudo-label refinement to reduce catastrophic forgetting in remote sensing incremental object detection, reporting 1.7% and 2.1% mAP gains on new DIOR-IOD and DOTA-IOD datasets.
-
Deep Learning-Based Computer Vision for Beam Selection and Proactive Blockage Prediction
Vision-aided deep learning delivers 98.96% beam prediction accuracy and over 98% proactive blockage prediction for mm-wave links, including the first treatment of simultaneous non-uniform mobility.
-
A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation
A scale-robust lightweight CNN for glottis segmentation achieves 92.9% mDice at over 170 FPS with a 19 MB model size on three datasets.
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DocRevive: A Unified Pipeline for Document Text Restoration
A unified pipeline using OCR, inpainting, and diffusion models restores text in degraded documents on a new synthetic benchmark dataset, evaluated with the proposed UCSM metric.
-
UAVDB: Point-Guided Masks for UAV Detection and Segmentation
Introduces UAVDB dataset for UAV detection/segmentation via PIC point-to-box conversion and SAM2 masks, with YOLO baselines showing PIC+SAM2 outperforms prior annotation methods on IoU.
-
Structure-Guided Mixed Masked Pretraining and Spatial Continuity Regularization for Printed Circuit Board Defect Detection
A new PCB defect detection method using structure-guided masked pretraining and spatial continuity regularization achieves 85.5% mAP0.5 on the DsPCBSD+ dataset.
-
Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
A hierarchically decoupled heterogeneous MoE framework with YOLO experts and lightweight gating network reports 76.8% mAP50-95 on a composite traffic sign dataset, a 2.3% gain over baseline with 39.4% lower compute.
-
Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents
Proposes a knowledge-adaptive edge expert agent architecture for sustainable biodiversity monitoring that separates visual perception from reasoning with an explicit knowledge base.
-
DFIR-DETR: Frequency-Domain Iterative Refinement and Dynamic Feature Aggregation for Small Object Detection
DFIR-DETR augments RT-DETR with frequency-domain iterative refinement and dynamic feature aggregation, reporting 92.9% mAP50 on NEU-DET and 51.6% on VisDrone at 11.7M parameters and 47.2 GFLOPs.
-
MinerU: An Open-Source Solution for Precise Document Content Extraction
MinerU delivers an open-source pipeline for high-precision document content extraction by integrating specialized models with tuned preprocessing and postprocessing rules.
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A Goal-Oriented Networking Approach for Intelligent IoT Service Deployment
A multi-objective optimization framework is proposed to assess KPIs in goal-oriented IoT service deployment, with simulation results indicating network efficiency benefits.
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A Comparative Study of Modern Object Detectors for Robust Apple Detection in Orchard Imagery
YOLO11n achieves the highest mAP@0.5:0.95 of 0.6065 for apple localization, with other detectors showing trade-offs in recall and precision at low confidence thresholds.
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Underwater Waste Detection Using Deep Learning A Performance Comparison of YOLOv7 to 10 and Faster RCNN
YOLOv8 achieves the highest mAP of 80.9% for detecting 15 classes of underwater waste among the tested models.
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YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review
Comparative review of YOLOv8 to YOLO11 architectures based on papers, docs, and code inspection, noting incremental improvements and some unchanged blocks.
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YOLOv11: An Overview of the Key Architectural Enhancements
YOLOv11 adds blocks such as C3k2, SPPF, and C2PSA to improve feature extraction, mAP, and efficiency while supporting detection, segmentation, pose, and oriented detection across model sizes.
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