New YolovN-CBi architecture enhances real-time detection of small UAVs

Researchers have developed a new lightweight architecture called YolovN-CBi, designed for real-time detection of small unmanned aerial vehicles (UAVs). This architecture integrates the Convolutional Block Attention Module (CBAM) and the Bidirectional Feature Pyramid Network (BiFPN) to enhance the detection of small objects. Evaluations on benchmark datasets and a custom local dataset show that the Yolov5-CBi variant outperforms newer YOLO versions like YOLOv8 and YOLOv12 in the speed-accuracy trade-off for small object detection. Furthermore, distilled versions of the CBi architecture, created using knowledge distillation, achieve significant improvements in accuracy and speed, making them suitable for edge deployment. AI

IMPACT This research could lead to more efficient and accurate real-time drone detection systems for security and civilian applications.

RANK_REASON The item is an academic paper detailing a new computer vision model architecture and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• Bidirectional Feature Pyramid Network
• BiFPN
• Convolutional Block Attention Module
• YOLOv12
• YOLOv5
• Yolov5m-CBi
• Yolov5n-CBi
• YOLOv8
• YolovN
• YolovN-CBi

• paper
• model release

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