has been cited by the following article(s):
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[1]
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Enhanced YOLOv11n for small object detection in UAV imagery: higher accuracy with fewer parameters
Scientific Reports,
2026
DOI:10.1038/s41598-026-35301-2
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[2]
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YOLOv11-Based Enhanced Framework for Robust Blasthole Detection in Intelligent Tunnel Environments
Journal of Computing in Civil Engineering,
2026
DOI:10.1061/JCCEE5.CPENG-7337
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[3]
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Cross-Modal Weak Supervision Training of UAV Detection Model for Degraded Environments
2026 49th MIPRO ICT and Electronics Convention (MIPRO),
2026
DOI:10.1109/MIPRO70003.2026.11591746
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[4]
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P2AW-YOLO11n: Improving Drone-Based Small Object Detection via AFPN and Enhanced Loss Functions
2025 International Conference on Cyberworlds (CW）,
2025
DOI:10.1109/CW68232.2025.00032
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[5]
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Enhanced Small Object Detection in Aerial Imagery: Integrating Pyramid Pooling Attention and Space-to-Depth Convolution in YOLOv8
2025 International Conference on Intelligent Robotics and Automatic Control (IRAC),
2025
DOI:10.1109/IRAC67707.2025.11381196
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[6]
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Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
Applied Sciences,
2025
DOI:10.3390/app15126919
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[7]
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YOLOv11-STDD: Improved YOLOv11 Small Target Detection for Drones
2025 7th International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI),
2025
DOI:10.1109/IoTAAI66837.2025.11213401
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