An improved lightweight tiny-person detection network based on YOLOv8: IYFVMNet DOI Creative Commons

Fan Yang,

Lihu Pan, Hongyan Cui

et al.

Frontiers in Physics, Journal Year: 2025, Volume and Issue: 13

Published: April 22, 2025

Introduction In the aviation field, drone search and rescue is a highly urgent task involving small target detection. such resource-constrained scenario, there are challenges of low accuracy high computational requirements. Methods This paper proposes IYFVMNet, an improved lightweight detection network based on YOLOv8. The key include feature extraction for objects trade-off between speed. To address these, four major innovations introduced: (1) Fasternet used to improve bottleneck structure in cross-stage fusion backbone network. approach fully utilizes all map information while minimizing memory (2) neck optimized using Vovnet Gsconv Cross Stage Partial module. operation also reduces cost by decreasing amount required channels, maintaining effectiveness representation. (3) he Minimum Point Distance Intersection over Union loss function employed optimize bounding box during model training. (4) construct overall structure, Layer-wise Adaptive Momentum Pruning algorithm thinning. Results Experiments TinyPerson dataset demonstrate that IYFVMNet achieves 46.3% precision, 30% recall, 29.3% mAP50, 11.8% mAP50-95. Discussion exhibits higher performance terms efficiency when compared other benchmark models, which demonstrates (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) small-object provides reference future research.

Language: Английский

An improved lightweight tiny-person detection network based on YOLOv8: IYFVMNet DOI Creative Commons

Fan Yang,

Lihu Pan, Hongyan Cui

et al.

Frontiers in Physics, Journal Year: 2025, Volume and Issue: 13

Published: April 22, 2025

Introduction In the aviation field, drone search and rescue is a highly urgent task involving small target detection. such resource-constrained scenario, there are challenges of low accuracy high computational requirements. Methods This paper proposes IYFVMNet, an improved lightweight detection network based on YOLOv8. The key include feature extraction for objects trade-off between speed. To address these, four major innovations introduced: (1) Fasternet used to improve bottleneck structure in cross-stage fusion backbone network. approach fully utilizes all map information while minimizing memory (2) neck optimized using Vovnet Gsconv Cross Stage Partial module. operation also reduces cost by decreasing amount required channels, maintaining effectiveness representation. (3) he Minimum Point Distance Intersection over Union loss function employed optimize bounding box during model training. (4) construct overall structure, Layer-wise Adaptive Momentum Pruning algorithm thinning. Results Experiments TinyPerson dataset demonstrate that IYFVMNet achieves 46.3% precision, 30% recall, 29.3% mAP50, 11.8% mAP50-95. Discussion exhibits higher performance terms efficiency when compared other benchmark models, which demonstrates (e.g., YOLO-SGF, Guo-Net, TRC-YOLO) small-object provides reference future research.

Language: Английский

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