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

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