ArticleAnimals : an open access journal from MDPI2025
MSFN-YOLOv11: A Novel Multi-Scale Feature Fusion Recognition Model Based on Improved YOLOv11 for Real-Time Monitoring of Birds in Wetland Ecosystems.
Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Automatic identification and feature analysis of Min-ing-Induced surface cracks using an improved DRA-UNet.Scientific reports · 2026Article
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Abstract
Intelligent bird species recognition is vital for biodiversity monitoring and ecological conservation. This study tackles the challenge of declining recognition accuracy caused by occlusions and imaging noise in complex natural environments. Focusing on ten representative bird species from the Dongting Lake Wetland, we propose an improved YOLOv11n-based model named MSFN-YOLO11, which incorporates multi-scale feature fusion. After selecting YOLOv11n as the baseline through comparison with the most-stable version of YOLOv8n, we enhance its backbone by introducing an MSFN module. This module strengthens global and local feature extraction via parallel dilated convolution and a channel attention mechanism. Experiments are conducted on a self-built dataset containing 4540 images of ten species with 6824 samples. To simulate real-world conditions, 25% of samples are augmented using random occlusion, Gaussian noise (σ = 0.2, 0.3, 0.4), and Poisson noise. The improved model achieves a
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