YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs

arXiv:2512.18046 · cs.CV · Submitted 2025-12-19 · Read on arXiv

cs.CV

Submitted: 2025-12-19

Updated: 2026-09-21

Code: https://github.com/ultralytics/yolov5

Terminology

• Distillation — 53× in this paper · explained in 2 episodes
• Knowledge Distillation — 25× in this paper · explained in 11 episodes
• Accuracy — 22× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Attention Mechanism — 10× in this paper · explained in 2 episodes
• Flying Object — 10× in this paper
• Edge Devices — 6× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Convolutional Block Attention Module — 5× in this paper
• Generalization — 5× in this paper · explained in 13 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Adversarial Knowledge Distillation — 3× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Channel Attention Module — 3× in this paper
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Edge Deployment — 3× in this paper · explained in 2 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Spatial Attention Module — 3× in this paper
• Adaptability — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Feature-based Knowledge Distillation — 2× in this paper
• Response-based Knowledge Distillation — 2× in this paper
• You Only Look Once — 2× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Long Range Drone Detection — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Self-distillation — 1× in this paper · explained in 6 episodes
• Single Shot Detector — 1× in this paper

Sources

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• R-FCN: Object Detection via Region-based Fully Convolutional Networks
• EfficientDet: Scalable and Efficient Object Detection
• Distilling the Knowledge in a Neural Network
• Mask R-CNN
• YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
• YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
• YOLOv10: Real-Time End-to-End Object Detection
• YOLOv11: An Overview of the Key Architectural Enhancements
• YOLOv12: Attention-Centric Real-Time Object Detectors
• YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions
• A Decade of You Only Look Once (YOLO) for Object Detection: A Review
• Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications
• Centerness-based Instance-aware Knowledge Distillation with Task-wise Mutual Lifting for Object Detection on Drone Imagery
• End-to-End Object Detection with Transformers
• Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
• Vision-based Anti-UAV Detection and Tracking

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